【论文列表】2025年Object Pose Estimation主要会议论文列表

CCF A

CVPR

1. Any6D: Model-free 6D Pose Estimation of Novel Objects
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Any6D_Model-free_6D_Pose_Estimation_of_Novel_Objects_CVPR_2025_paper.html
   • 代码：https://github.com/taeyeopl/Any6D
   • 摘要：We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, Toyota-Light, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d.
2. CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image
   • 分类：类别级铰接部件方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Huang_CAP-Net_A_Unified_Network_for_6D_Pose_and_Size_Estimation_CVPR_2025_paper.html
   • 代码：https://github.com/ShaneHuangHZ/CAPNet
   • 摘要：This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation for 6D poses. These approaches overlook dense semantic cues from RGB images, leading to suboptimal accuracy, particularly for objects with small parts. To address these limitations, we propose a single-stage Network, CAP-Net, for estimating the 6D poses and sizes of Categorical Articulated Parts. This method combines RGB-D features to generate instance segmentation and NPCS representations for each part in an end-to-end manner. CAP-Net uses a unified network to simultaneously predict point-wise class labels, centroid offsets, and NPCS maps. A clustering algorithm then groups points of the same predicted class based on their estimated centroid distances to isolate each part. Finally, the NPCS region of each part is aligned with the point cloud to recover its final pose and size. To bridge the sim-to-real domain gap, we introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors. Experimental evaluations on the RGBD-Art dataset demonstrate that our method significantly outperforms the state-of-the-art approach. Real-world deployments of our model in robotic tasks underscore its robustness and exceptional sim-to-real transfer capabilities, confirming its substantial practical utility. Our dataset, code and pre-trained models are available on the project page.
3. Co-op: Correspondence-based Novel Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Moon_Co-op_Correspondence-based_Novel_Object_Pose_Estimation_CVPR_2025_paper.html
   • 代码：代码未开源
   • 摘要：We propose Co-op, a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image. Our method requires only the CAD model of the target object and can precisely estimate its pose without any additional fine-tuning. While existing model-based methods suffer from inefficiency due to using a large number of templates, our method enables fast and accurate estimation with a small number of templates. This improvement is achieved by finding semi-dense correspondences between the input image and the pre-rendered templates. Our method achieves strong generalization performance by leveraging a hybrid representation that combines patch-level classification and offset regression. Additionally, our pose refinement model estimates probabilistic flow between the input image and the rendered image, refining the initial estimate to an accurate pose using a differentiable PnP layer. We demonstrate that our method not only estimates object poses rapidly but also outperforms existing methods by a large margin on the seven core datasets of the BOP Challenge, achieving state-of-the-art accuracy.
4. CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Shi_CRISP_Object_Pose_and_Shape_Estimation_with_Test-Time_Adaptation_CVPR_2025_paper.html
   • 代码：https://github.com/MIT-SPARK/CRISP
   • 摘要：We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction and a DPT-based network for estimating pose-normalized points for pose estimation. As a second contribution, we propose an optimization-based pose and shape corrector that can correct estimation errors caused by a domain gap. Observing that the shape decoder is well behaved in the convex hull of known shapes, we approximate the shape decoder with an active shape model, and show that this reduces the shape correction problem to a constrained linear least squares problem, which can be solved efficiently by an interior point algorithm. Third, we introduce a self-training pipeline to perform self-supervised domain adaptation of CRISP. The self-training is based on a correct-and-certify approach, which leverages the corrector to generate pseudo-labels at test time, and uses them to self-train CRISP. We demonstrate CRISP (and the self-training) on YCBV, SPE3R, and NOCS datasets. CRISP shows high performance on all the datasets. Moreover, our self-training is capable of bridging a large domain gap. Finally, CRISP also shows an ability to generalize to unseen objects. Code, pre-trained models and videos of sample results are available on the project webpage.
5. GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
   • 分类：类别级方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Li_GCE-Pose_Global_Context_Enhancement_for_Category-level_Object_Pose_Estimation_CVPR_2025_paper.html
   • 代码：代码未开源
   • 摘要：A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Recent approaches leverage foundational features to capture semantic and geometry cues from data. However, these approaches fail under partial visibility. We overcome this with a first-complete-then-aggregate strategy for feature extraction utilizing class priors. In this paper, we present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior. GCE-Pose performs semantic shape reconstruction with a proposed Semantic Shape Reconstruction (SSR) module. Given an unseen partial RGB-D object instance, our SSR module reconstructs the instance’s global geometry and semantics by deforming category-specific 3D semantic prototypes through a learned deep Linear Shape Model. We further introduce a Global Context Enhanced (GCE) feature fusion module that effectively fuses features from partial RGB-D observations and the reconstructed global context. Extensive experiments validate the impact of our global context prior and the effectiveness of the GCE fusion module, demonstrating that GCE-Pose significantly outperforms existing methods on challenging real-world datasets House-Cat6D and NOCS-REAL275. Our project page is available at https://colin-de.github.io/GCE-Pose/.
6. GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation
   • 分类：类别级方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Huang_GIVEPose_Gradual_Intra-class_Variation_Elimination_for_RGB-based_Category-Level_Object_Pose_CVPR_2025_paper.html
   • 代码：https://github.com/ziqin-h/GIVEPose
   • 摘要：Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from instance-level tasks has demonstrated strong performance. However, we argue that the NOCS map is an inadequate intermediate representation for geometry-guided pose regression method, as its many-to-one correspondence with category-level pose introduces redundant instance-specific information, resulting in suboptimal results. This paper identifies the intra-class variation problem inherent in pose regression based solely on the NOCS map and proposes the Intra-class Variation-Free Consensus (IVFC) map, a novel coordinate representation generated from the category-level consensus model. By leveraging the complementary strengths of the NOCS map and the IVFC map, we introduce GIVEPose, a framework that implements Gradual Intra-class Variation Elimination for category-level object pose estimation. Extensive evaluations on both synthetic and real-world datasets demonstrate that GIVEPose significantly outperforms existing state-of-the-art RGB-based approaches, achieving substantial improvements in category-level object pose estimation. Our code is available at https://github.com/ziqin-h/GIVEPose.
7. iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Cao_iG-6DoF_Model-free_6DoF_Pose_Estimation_for_Unseen_Object_via_Iterative_CVPR_2025_paper.html
   • 代码：代码未开源
   • 摘要：Traditional methods in pose estimation often rely on precise 3D models or additional data such as depth and normals, limiting their generalization, especially when objects undergo large translations or rotations. We propose iG-6DoF, a novel model-free 6D pose estimation method using iterative 3D Gaussian Splatting to estimate the pose of unseen objects. We first estimates an initial pose by leveraging multi-scale data augmentation and the rotation-equivariant features to create a better pose hypothesis from a set of candidates. Then, we propose an iterative 3DGS approach through iteratively rendering and comparing the rendered image with the input image to further progressively improve pose estimation accuracy. The proposed method consists of an object detector, a multi-scale rotation-equivariant feature based initial pose estimator, and a coarse-to-fine pose refiner. Such combination allows our method to focus on the target object in a complex scene dealing with large movement and weak textures. Our method achieves state-of-the-art results on the LINEMOD, OnePose-LowTexture, GenMOP datasets and our self-captured data, demonstrating its strong generalization to unseen objects and robustness across various scenes.
8. Leveraging Global Stereo Consistency for Category-Level Shape and 6D Pose Estimation from Stereo Images
   • 分类：类别级方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Leveraging_Global_Stereo_Consistency_for_Category-Level_Shape_and_6D_Pose_CVPR_2025_paper.html
   • 代码：代码未开源
   • 摘要：Stereo-based category-level shape and 6D pose estimation methods have the potential to generalize to a wider range of materials than RGBD methods, which often suffer from depth measurement errors. However, without explicit depth from two views, parameters to be estimated can become inherently entangled, negatively impacting performance. To address this, we propose a method that leverages global stereo consistency to constrain optimization directions and mitigate parameter entanglement. We first estimate an intra-category occupancy field to represent a unified shape across views, ensuring consistency and preventing shape ambiguity. Through a divide-and-conquer approach within global shape fitting, we fit this shape to stereo images to obtain the pose, iteratively rendering normalized depth maps and exchanging information across views. This approach improves convergence toward the correct pose and scale. We validated our method on both depth-friendly and depth-challenging materials using our S-RGBD dataset and the TOD benchmark. Our method surpasses RGBD methods on challenging objects and performs comparably on depth-friendly ones. Ablation studies confirm the effectiveness of each component.
9. ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation
   • 分类：实例级方法
   • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Tan_ONDA-Pose_Occlusion-Aware_Neural_Domain_Adaptation_for_Self-Supervised_6D_Object_Pose_CVPR_2025_paper.html
   • 代码：https://github.com/tan-tao11/ONDA-Pose
   • 摘要：Self-supervised 6D object pose estimation has received increasing attention in computer vision recently. Some typical works in literature attempt to translate the synthetic images with object pose labels generated by object CAD models into the real domain, and then use the translated data for training. However, their performance is generally limited, since (i) there still exists a domain gap between the translated images and the real images and (ii) the translated images can not sufficiently reflect occlusions that exist in many real images. To address these problems, we propose an Occlusion-Aware Neural Domain Adaptation method for self-supervised 6D object Pose estimation, called ONDA-Pose. The proposed method comprises three main steps. Firstly, by utilizing both the training real images without pose labels and a CAD model, we explore a CAD-like radiance field for rendering corresponding synthetic images that have similar textures to those generated by the CAD model. Then, a backbone pose estimator trained on the synthetic data is employed to provide initial pose estimations for the synthetic images rendered from the CAD-like radiance field, and the initial object poses are refined by a global object pose refiner to generate pseudo object pose labels. Finally, the backbone pose estimator is further self-supervised as the final pose estimator by jointly utilizing the real images with pseudo object pose labels and the synthetic images rendered from the CAD-like radiance field. Experimental results on three public datasets demonstrate that ONDA-Pose significantly outperforms the comparative state-of-the-art methods in most cases.
10. One2Any: One-Reference 6D Pose Estimation for Any Object
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Liu_One2Any_One-Reference_6D_Pose_Estimation_for_Any_Object_CVPR_2025_paper.html
    • 代码：https://github.com/lmy1001/One2Any
    • 摘要：6D object pose estimation remains challenging for many applications due to dependencies on complete 3D models, multi-view images, or training limited to specific object categories. These requirements make generalization to novel objects difficult for which neither 3D models nor multi-view images may be available. To address this, we propose a novel method One2Any that estimates the relative 6-degrees of freedom (DOF) object pose using only a single reference-single query RGB-D image, without prior knowledge of its 3D model, multi-view data, or category constraints. We treat object pose estimation as an encoding-decoding process: first, we obtain a comprehensive Reference Object Pose Embedding (ROPE) that encodes an object’s shape, orientation, and texture from a single reference view. Using this embedding, a U-Net-based pose decoding module produces Reference Object Coordinate (ROC) for new views, enabling fast and accurate pose estimation. This simple encoding-decoding framework allows our model to be trained on any pair-wise pose data, enabling large-scale training and demonstrating great scalability. Experiments on multiple benchmark datasets demonstrate that our model generalizes well to novel objects, achieving state-of-the-art accuracy and robustness even rivaling methods that require multi-view or CAD inputs, at a fraction of compute. Code is available at https://github.com/lmy1001/One2Any.
11. Pos3R: 6D Pose Estimation for Unseen Objects Made Easy
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Pos3R_6D_Pose_Estimation_for_Unseen_Objects_Made_Easy_CVPR_2025_paper.html
    • 代码：代码未开源
    • 摘要：Foundation models have significantly reduced the need for task-specific training, while also enhancing generalizability. However, state-of-the-art 6D pose estimators either require further training with pose supervision or neglect advances obtainable from 3D foundation models. The latter is a missed opportunity, since these models are better equipped to predict 3D-consistent features, which are of significant utility for the pose estimation task. To address this gap, we propose Pos3R, a method for estimating the 6D pose of any object from a single RGB image, making extensive use of a 3D reconstruction foundation model and requiring no additional training. We identify template selection as a particular bottleneck for existing methods that is significantly alleviated by the use of a 3D model, which can more easily distinguish between template poses than a 2D model. Despite its simplicity, Pos3R achieves competitive performance on the Benchmark for 6D Object Pose Estimation (BOP), matching or surpassing existing refinement-free methods. Additionally, Pos3R integrates seamlessly with render-and-compare refinement techniques, demonstrating adaptability for high-precision applications.
12. RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Kim_RefPose_Leveraging_Reference_Geometric_Correspondences_for_Accurate_6D_Pose_Estimation_CVPR_2025_paper.html
    • 代码：代码未开源
    • 摘要：Estimating the 6D pose of unseen objects from monocular RGB images remains a challenging problem, especially due to the lack of prior object-specific knowledge. To tackle this issue, we propose RefPose, an innovative approach to object pose estimation that leverages a reference image and geometric correspondence as guidance. RefPose first predicts an initial pose by using object templates to render the reference image and establish the geometric correspondence needed for the refinement stage. During the refinement stage, RefPose estimates the geometric correspondence of the query image based on the generated references and iteratively refines the pose through a render-and-compare approach. To enhance this estimation, we introduce a correlation volume-guided attention mechanism that effectively captures correlations between the query and reference images. Unlike traditional methods that depend on pre-defined object models, RefPose dynamically adapts to new object shapes by leveraging a reference image and geometric correspondence. This results in robust performance across previously unseen objects. Extensive evaluation on the BOP benchmark datasets shows that RefPose achieves state-of-the-art results while maintaining a competitive runtime.
13. Rethinking Correspondence-based Category-Level Object Pose Estimation
    • 分类：类别级方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Rethinking_Correspondence-based_Category-Level_Object_Pose_Estimation_CVPR_2025_paper.html
    • 代码：代码未开源
    • 摘要：Category-level object pose estimation aims to determine the pose and size of arbitrary objects within given categories. Existing two-stage correspondence-based methods first establish correspondences between camera and object coordinates, and then acquire the object pose using a pose fitting algorithm. In this paper, we conduct a comprehensive analysis of this paradigm and introduce two crucial essentials: 1) shape-sensitive and pose-invariant feature extraction for accurate correspondence prediction, and 2) outlier correspondence removal for robust pose fitting. Based on these insights, we propose a simple yet effective correspondence-based method called SpotPose, which includes two stages. During the correspondence prediction stage, pose-invariant geometric structure of objects is thoroughly exploited to facilitate shape-sensitive holistic interaction among keypoint-wise features. During the pose fitting stage, outlier scores of correspondences are explicitly predicted to facilitate efficient identification and removal of outliers. Experimental results on CAMERA25, REAL275 and HouseCat6D benchmarks demonstrate that the proposed SpotPose outperforms state-of-the-art approaches by a large margin.
14. SCFlow2: Plug-and-Play Object Pose Refiner with Shape-Constraint Scene Flow
    • 分类：任意物体优化方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SCFlow2_Plug-and-Play_Object_Pose_Refiner_with_Shape-Constraint_Scene_Flow_CVPR_2025_paper.html
    • 代码：https://github.com/W-QY/SCFlow2
    • 摘要：We introduce SCFlow2, a plug-and-play refinement framework for 6D object pose estimation. Most recent 6D object pose methods rely on refinement to get accurate results. However, most existing refinement methods either suffer from noises in establishing correspondences, or rely on retraining for novel objects. SCFlow2 is based on the SCFlow model designed for refinement with shape constraint, but formulates the additional depth as a regularization in the iteration via 3D scene flow for RGBD frames. The key design of SCFlow2 is an introduction of geometry constraints into the training of recurrent matching network, by combining the rigid-motion embeddings in 3D scene flow and 3D shape prior of the target. We train SCFlow2 on a combination of dataset Objaverse, GSO and ShapeNet, and evaluate on BOP datasets with novel objects. After using our method as a post-processing, most state-of-the-art methods produce significantly better results, without any retraining or fine-tuning. The source code is available at https://scflow2.github.io.
15. Structure-Aware Correspondence Learning for Relative Pose Estimation
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Structure-Aware_Correspondence_Learning_for_Relative_Pose_Estimation_CVPR_2025_paper.html
    • 代码：https://github.com/Cyhhzo02/SAC-Pose-code
    • 摘要：Relative pose estimation provides a promising way for achieving object-agnostic pose estimation. Despite the success of existing 3D correspondence-based methods, the reliance on explicit feature matching suffers from small overlaps in visible regions and unreliable feature estimation for invisible regions. Inspired by humans’ ability to assemble two object parts that have small or no overlapping regions by considering object structure, we propose a novel Structure-Aware Correspondence Learning method for Relative Pose Estimation, which consists of two key modules. First, a structure-aware keypoint extraction module is designed to locate a set of keypoints that can represent the structure of objects with different shapes and appearance, under the guidance of a keypoint based image reconstruction loss. Second, a structure-aware correspondence estimation module is designed to model the intra-image and inter-image relationships between keypoints to extract structure-aware features for correspondence estimation. By jointly leveraging these two modules, the proposed method can naturally estimate 3D-3D correspondences for unseen objects without explicit feature matching for precise relative pose estimation. Experimental results on the CO3D, Objaverse and LineMOD datasets demonstrate that the proposed method significantly outperforms prior methods, i.e., with 5.7° reduction in mean angular error on the CO3D dataset.
16. UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Li_UA-Pose_Uncertainty-Aware_6D_Object_Pose_Estimation_and_Online_Object_Completion_CVPR_2025_paper.html
    • 代码：代码未开源
    • 摘要：6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous reference images that fully cover the object. Estimating 6D poses from partial references, which capture only fragments of an object’s appearance and geometry, remains challenging. To address this, we propose UA-Pose, an uncertainty-aware approach for 6D object pose estimation and online object completion specifically designed for partial references. We assume access to either (1) a limited set of RGBD images with known poses or (2) a single 2D image. For the first case, we initialize a partial object 3D model based on the provided images and poses, while for the second, we use image-to-3D techniques to generate an initial object 3D model. Our method integrates uncertainty into the incomplete 3D model, distinguishing between seen and unseen regions. This uncertainty enables confidence assessment in pose estimation and guides an uncertainty-aware sampling strategy for online object completion, enhancing robustness in pose estimation accuracy and improving object completeness. We evaluate our method on the YCB-Video, YCBInEOAT, and HO3D datasets, including RGBD sequences of YCB objects manipulated by robots and human hands. Experimental results demonstrate significant performance improvements over existing methods, particularly when object observations are incomplete or partially captured. Project page: https://minfenli.github.io/UA-Pose/.
17. UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
    • 分类：任意物体方法
    • 论文：https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UNOPose_Unseen_Object_Pose_Estimation_with_an_Unposed_RGB-D_Reference_CVPR_2025_paper.html
    • 代码：https://github.com/shanice-l/UNOPose
    • 摘要：Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimate the unseen object’s pose through a single unposed RGB-D reference image. While previous works leverage reference images as pose anchors to limit the range of relative pose, our scenario presents significant challenges since the relative transformation could vary across the entire SE(3) space. Moreover, factors like occlusion, sensor noise, and extreme geometry could result in low viewpoint overlap. To address these challenges, we present a novel approach and benchmark, termed UNOPose, for UNseen One-reference-based object Pose estimation. Building upon a coarse-to-fine paradigm, UNOPose constructs an SE(3)-invariant reference frame to standardize object representation despite pose and size variations. To alleviate small overlap across viewpoints, we recalibrate the weight of each correspondence based on its predicted likelihood of being within the overlapping region. Evaluated on our proposed benchmark based on the BOP Challenge, UNOPose demonstrates superior performance, significantly outperforming traditional and learning-based methods in the one-reference setting and remaining competitive with CAD-model-based methods. The code and dataset are available at github.com/shanice-l/UNOPose.

NeurIPS

1. RFMPose: Generative Category-level Object Pose Estimation via Riemannian Flow Matching
   • 分类：类别级方法
   • 论文：https://neurips.cc/virtual/2025/poster/119779
   • 代码：https://github.com/shabiouyang/RMFPose
   • 摘要：We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e.g., symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations: (1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints; (2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design; (3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies. Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of +4.1 in IoU25 and +2.4 in 5°2cm metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.
2. SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference
   • 分类：任意物体方法
   • 论文：https://neurips.cc/virtual/2025/poster/115633
   • 代码：代码未开源
   • 摘要：Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail with challenging surface conditions, such as transparent or highly reflective materials. In the meantime, RGB-based solutions provide less robust matching performance in low-light and texture-less scenes due to the lack of geometry information. Motivated by these, we propose SingRef6D, a lightweight pipeline requiring only a single RGB image as a reference, eliminating the need for costly depth sensors, multi-view image acquisition, or training view synthesis models and neural fields. This enables SingRef6D to remain robust and capable even under resource-limited settings where depth or dense templates are unavailable. Our framework incorporates two key innovations. First, we propose a token-scaler-based fine-tuning mechanism with a novel optimization loss on top of Depth-Anything v2 to enhance its ability to predict accurate depth, even for challenging surfaces. Our results show a 14.41% improvement in (δ1.05) on REAL275 depth prediction compared to Depth-Anything v2 (with fine-tuned head). Second, benefiting from depth availability, we introduce a depth-aware matching process that effectively integrates spatial relationships within LoFTR, enabling our system to handle matching for challenging materials and lighting conditions. Evaluations of pose estimation on the REAL275, ClearPose, and Toyota-Light datasets show that our approach surpasses state-of-the-art methods, achieving a 6.1% improvement in average recall.

ICLR

1. 6D Object Pose Tracking in Internet Videos for Robotic Manipulation
   • 分类：任意物体跟踪方法
   • 论文：https://openreview.net/pdf?id=1CIUkpoata
   • 代码：https://github.com/ponimatkin/freepose
   • 摘要：We seek to extract a temporally consistent 6D pose trajectory of a manipulated object from an Internet instructional video. This is a challenging set-up for current 6D pose estimation methods due to uncontrolled capturing conditions, subtle but dynamic object motions, and the fact that the exact mesh of the manipulated object is not known. To address these challenges, we present the following contributions. First, we develop a new method that estimates the 6D pose of any object in the input image without prior knowledge of the object itself. The method proceeds by (i) retrieving a CAD model similar to the depicted object from a large-scale model database, (ii) 6D aligning the retrieved CAD model with the input image, and (iii) grounding the absolute scale of the object with respect to the scene. Second, we extract smooth 6D object trajectories from Internet videos by carefully tracking the detected objects across video frames. The extracted object trajectories are then retargeted via trajectory optimization into the configuration space of a robotic manipulator. Third, we thoroughly evaluate and ablate our 6D pose estimation method on YCB-V and HOPE-Video datasets as well as a new dataset of instructional videos manually annotated with approximate 6D object trajectories. We demonstrate significant improvements over existing state-of-the-art RGB 6D pose estimation methods. Finally, we show that the 6D object motion estimated from Internet videos can be transferred to a 7-axis robotic manipulator both in a virtual simulator as well as in a real world set-up. We also successfully apply our method to egocentric videos taken from the EPIC-KITCHENS dataset, demonstrating potential for Embodied AI applications.
2. Learning Shape-Independent Transformation via Spherical Representations for Category-Level Object Pose Estimation
   • 分类：类别级方法
   • 论文：https://openreview.net/pdf?id=D4xztKoz0Y
   • 代码：代码未开源
   • 摘要：Category-level object pose estimation aims to determine the pose and size of novel objects in specific categories. Existing correspondence-based approaches typically adopt point-based representations to establish the correspondences between primitive observed points and normalized object coordinates. However, due to the inherent shape-dependence of canonical coordinates, these methods suffer from semantic incoherence across diverse object shapes. To resolve this issue, we innovatively leverage the sphere as a shared proxy shape of objects to learn shape-independent transformation via spherical representations. Based on this insight, we introduce a novel architecture called SpherePose, which yields precise correspondence prediction through three core designs. Firstly, We endow the point-wise feature extraction with SO(3)-invariance, which facilitates robust mapping between camera coordinate space and object coordinate space regardless of rotation transformation. Secondly, the spherical attention mechanism is designed to propagate and integrate features among spherical anchors from a comprehensive perspective, thus mitigating the interference of noise and incomplete point cloud. Lastly, a hyperbolic correspondence loss function is designed to distinguish subtle distinctions, which can promote the precision of correspondence prediction. Experimental results on CAMERA25, REAL275 and HouseCat6D benchmarks demonstrate the superior performance of our method, verifying the effectiveness of spherical representations and architectural innovations.

ICML

ICCV

1. 6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Jin_6DOPE-GS_Online_6D_Object_Pose_Estimation_using_Gaussian_Splatting_ICCV_2025_paper.pdf
   • 代码：代码未开源
   • 摘要：Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics. While research in model-based 6D object pose estimation has delivered promising results, model-free methods are hindered by the high computational load in rendering and inferring consistent poses of arbitrary objects in a live RGB-D video stream. To address this issue, we present 6DOPE-GS, a novel method for online 6D object pose estimation & tracking with a single RGB-D camera by effectively leveraging advances in Gaussian Splatting. Thanks to the fast differentiable rendering capabilities of Gaussian Splatting, our method can simultaneously optimize for 6D object poses and 3D object reconstruction. To achieve the necessary efficiency and accuracy for live tracking, our method uses incremental 2D Gaussian Splatting with an intelligent dynamic keyframe selection procedure to achieve high spatial object coverage and prevent erroneous pose updates. We also propose an opacity statistic-based pruning mechanism for adaptive Gaussian density control, to ensure training stability and efficiency. We evaluate our method on the HO3D and YCBInEOAT datasets and show that 6DOPE-GS matches the performance of state-of-the-art baselines for model-free simultaneous 6D pose tracking and reconstruction while providing a 5x speedup. We also demonstrate the method’s suitability for live, dynamic object tracking and reconstruction in a real-world setting.
2. BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_BoxDreamer_Dreaming_Box_Corners_for_Generalizable_Object_Pose_Estimation_ICCV_2025_paper.pdf
   • 代码：https://github.com/zju3dv/BoxDreamer
   • 摘要：This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications.
3. CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
   • 分类：类别级方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Lin_CleanPose_Category-Level_Object_Pose_Estimation_via_Causal_Learning_and_Knowledge_ICCV_2025_paper.pdf
   • 代码：https://github.com/chrislin0621/CleanPose
   • 摘要：In the effort to achieve robust and generalizable category-level object pose estimation, recent methods primarily focus on learning fundamental representations from data. However, the inherent biases within the data are often overlooked: the repeated training samples and similar environments may mislead the models to over-rely on specific patterns, hindering models’ performance on novel instances. In this paper, we present CleanPose, a novel method that mitigates the data biases to enhance category-level pose estimation by integrating causal learning and knowledge distillation. By incorporating key causal variables (structural information and hidden confounders) into causal modeling, we propose the causal inference module based on front-door adjustment, which promotes unbiased estimation by reducing potential spurious correlations. Additionally, to further confront the data bias at the feature level, we devise a residual-based knowledge distillation approach to transfer unbiased semantic knowledge from 3D foundation model, providing comprehensive causal supervision. Extensive experiments across multiple benchmarks (REAL275, CAMERA25 and HouseCat6D) highlight the superiority of proposed CleanPose over state-of-the-art methods. Code will be available at https://github.com/chrislin0621/CleanPose.
4. Deterministic Object Pose Confidence Region Estimation
   • 分类：实例级方法
   • 论文：https://arxiv.org/pdf/2506.22720
   • 代码：代码未开源
   • 摘要：6D pose confidence region estimation has emerged as a critical direction, aiming to perform uncertainty quantification for assessing the reliability of estimated poses. However, current sampling-based approach suffers from critical limitations that severely impede their practical deployment: 1) the sampling speed significantly decreases as the number of samples increases. 2) the derived confidence regions are often excessively large. To address these challenges, we propose a deterministic and efficient method for estimating pose confidence regions. Our approach uses inductive conformal prediction to calibrate the deterministically regressed Gaussian keypoint distributions into 2D keypoint confidence regions. We then leverage the implicit function theorem to propagate these keypoint confidence regions directly into 6D pose confidence regions. This method avoids the inefficiency and inflated region sizes associated with sampling and ensembling. It provides compact confidence regions that cover the ground-truth poses with a user-defined confidence level. Experimental results on the LineMOD Occlusion and SPEED datasets show that our method achieves higher pose estimation accuracy with reduced computational time. For the same coverage rate, our method yields significantly smaller confidence region volumes, reducing them by up to 99.9% for rotations and 99.8% for translations. The code will be available soon.
5. Environment-Agnostic Pose: Generating Environment-independent Object Representations for 6D Pose Estimation
   • 分类：实例级方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Environment-Agnostic_Pose_Generating_Environment-independent_Object_Representations_for_6D_Pose_Estimation_ICCV_2025_paper.pdf
   • 代码：https://github.com/acmff22/EA6D
   • 摘要：This paper introduces EA6D, a novel diffusion-based framework for 6D pose estimation that operates effectively in any environment. Traditional pose estimation methods struggle with the variability and complexity of real-world scenarios, often leading to overfitting on controlled datasets and poor generalization to new scenes. To address these challenges, we propose a generative pose estimation paradigm that generates environment-independent object representations for pose estimation, which are robust to environmental variations such as illumination, occlusion, and background clutter. Specifically, we propose the novel Environment Decoupling Diffusion Model (EDDM) which separates object representations from environmental factors while enabling efficient few-step sampling by leveraging input image priors instead of pure noise initialization. We validate our approach on four standard benchmarks and a self-made dataset DiverseScenes. The results demonstrate that EA6D, trained using only synthetic data, can outperform the state-of-the-art methods with both synthetic and realistic data. In particular, for fair comparisons with synthetic data, we can exceed the previous SOTA by 18.1% and 33.5% on Linemod and Linemod-Occluded datasets respectively. Project page: https://github.com/acmff22/EA6D.
6. HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
   • 分类：实例级方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_HccePoseBF_Predicting_Front__Back_Surfaces_to_Construct_Ultra-Dense_2D-3D_ICCV_2025_paper.pdf
   • 代码：https://github.com/WangYuLin-SEU/HCCEPose
   • 摘要：In pose estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images, which are then used to establish dense 2D-3D correspondences. However, current methods primarily focus on more efficient encoding techniques to improve the precision of predicted 3D coordinates on the object’s front surface, overlooking the potential benefits of incorporating the back surface and interior of the object. To better utilize the full surface and interior of the object, this study predicts 3D coordinates of both the object’s front and back surfaces and densely samples 3D coordinates between them. This process creates ultra-dense 2D-3D correspondences, effectively enhancing pose estimation accuracy based on the Perspective-n-Point (PnP) algorithm. Additionally, we propose Hierarchical Continuous Coordinate Encoding (HCCE) to provide a more accurate and efficient representation of front and back surface coordinates. Experimental results show that, compared to existing state-of-the-art (SOTA) methods on the BOP website, the proposed approach outperforms across seven classic BOP core datasets. Code is available at https://github.com/WangYuLin-SEU/HCCEPose.
7. Joint Learning of Pose Regression and Denoising Diffusion with Score Scaling Sampling for Category-level 6D Pose Estimation
   • 分类：类别级方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Lee_Joint_Learning_of_Pose_Regression_and_Denoising_Diffusion_with_Score_ICCV_2025_paper.pdf
   • 代码：代码未开源
   • 摘要：Latest diffusion models have shown promising results in category-level 6D object pose estimation by modeling the conditional pose distribution with depth image input. The existing methods, however, suffer from slow convergence during training, learning its encoder with the diffusion denoising network in end-to-end fashion, and require an additional network that evaluates sampled pose hypotheses to filter out low-quality pose candidates. In this paper, we propose a novel pipeline that tackles these limitations by two key components. First, the proposed method pre-trains the encoder with the direct pose regression head, and jointly learns the networks via the regression head and the denoising diffusion head, significantly accelerating training convergence while achieving higher accuracy. Second, sampling guidance via time-dependent score scaling is proposed s.t. the exploration-exploitation trade-off is effectively taken, eliminating the need for the additional evaluation network. The sampling guidance maintains multimodal characteristics of symmetric objects at early denoising steps while ensuring high-quality pose generation at final steps. Extensive experiments on multiple benchmarks including REAL275, HouseCat6D, and ROPE, demonstrate that the proposed method, simple yet effective, achieves state-of-the-art accuracies even with single-pose inference, while being more efficient in both training and inference.
8. MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_MixRI_Mixing_Features_of_Reference_Images_for_Novel_Object_Pose_ICCV_2025_paper.pdf
   • 代码：代码未开源
   • 摘要：We present MixRI, a lightweight network that solves the CAD-based novel object pose estimation problem in RGB images. It can be instantly applied to a novel object at test time without finetuning. We design our network to meet the demands of real-world applications, emphasizing reduced memory requirements and fast inference time. Unlike existing works that utilize many reference images and have large network parameters, we directly match points based on the multi-view information between the query and reference images with a lightweight network. Thanks to our reference image fusion strategy, we significantly decrease the number of reference images, thus decreasing the time needed to process these images and the memory required to store them. Furthermore, with our lightweight network, our method requires less inference time. Though with fewer reference images, experiments on seven core datasets in the BOP challenge show that our method achieves comparable results with other methods that require more reference images and larger network parameters.
9. RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_RayPose_Ray_Bundling_Diffusion_for_Template_Views_in_Unseen_6D_ICCV_2025_paper.pdf
   • 代码：代码未开源
   • 摘要：Typical template-based object pose pipelines estimate the pose by retrieving the closest matching template and aligning it with the observed image. However, failure to retrieve the correct template often leads to inaccurate pose predictions. To address this, we reformulate template-based object pose estimation as a ray alignment problem, where the viewing directions from multiple posed template images are learned to align with a non-posed query image. Inspired by recent progress in diffusion-based camera pose estimation, we embed this formulation into a diffusion transformer architecture that aligns a query image with a set of posed templates. We reparameterize object rotation using object-centered camera rays and model object translation by extending scale-invariant translation estimation to dense translation offsets. Our model leverages geometric priors from the templates to guide accurate query pose inference. A coarse-to-fine training strategy based on narrowed template sampling improves performance without modifying the network architecture. Extensive experiments across multiple benchmark datasets show competitive results of our method compared to state-of-the-art approaches in unseen object pose estimation.
10. Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypes
    • 分类：类别级方法
    • 论文：https://openaccess.thecvf.com/content/ICCV2025/papers/Fischer_Unified_Category-Level_Object_Detection_and_Pose_Estimation_from_RGB_Images_ICCV_2025_paper.pdf
    • 代码：https://github.com/Fischer-Tom/unified-detection-and-pose-estimation
    • 摘要：Recognizing objects in images is a fundamental problem in computer vision. Although detecting objects in 2D images is common, many applications require determining their pose in 3D space. Traditional category-level methods rely on RGB-D inputs, which may not always be available, or employ two-stage approaches that use separate models and representations for detection and pose estimation. For the first time, we introduce a unified model that integrates detection and pose estimation into a single framework for RGB images by leveraging neural mesh models with learned features and multi-model RANSAC. Our approach achieves state-of-the-art results for RGB category-level pose estimation on REAL275, improving on the current state-of-the-art by 22.9% averaged across all scale-agnostic metrics. Finally, we demonstrate that our unified method exhibits greater robustness compared to single-stage baselines. Our code and models are available at github.com/Fischer-Tom/unified-detection-and-pose-estimation.

AAAI

1. Free-Moving Object Reconstruction and Pose Estimation with Virtual Camera
   • 分类：任意物体方法
   • 论文：https://ojs.aaai.org/index.php/AAAI/article/view/32736
   • 代码：https://github.com/HaixinShi/fmov_pose
   • 摘要：We propose an approach for reconstructing free-moving object from a monocular RGB video. Most existing methods either assume scene prior, hand pose prior, object category pose prior, or rely on local optimization with multiple sequence segments. We propose a method that allows free interaction with the object in front of a moving camera without relying on any prior, and optimizes the sequence globally without any segments. We progressively optimize the object shape and pose simultaneously based on an implicit neural representation. A key aspect of our method is a virtual camera system that reduces the search space of the optimization significantly. We evaluate our method on the standard HO3D dataset and a collection of egocentric RGB sequences captured with a head-mounted device. We demonstrate that our approach outperforms most methods significantly, and is on par with recent techniques that assume prior information.
2. KeyPose: Category-Level 6D Object Pose Estimation with Self-Adaptive Keypoints
   • 分类：类别级方法
   • 论文：https://ojs.aaai.org/index.php/AAAI/article/view/33046
   • 代码：https://github.com/BIT-robot-group/KeyPose
   • 摘要：Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this paper, we first introduce a transformer-based keypoint prediction method for adaptive forecasting of point cloud keypoints. This method calculates the similarity between keypoint features and point cloud features, allowing keypoints to represent object geometry more effectively. Furthermore, to enhance the geometric feature construction of keypoints, we propose a graph-based keypoint feature aggregation method, which considers both the structural relationships between keypoints and the point cloud, strengthening the network’s understanding of geometric structures. At this stage, keypoints remain at the geometric spatial level of the object and have not been predicted in NOCS. To improve the accuracy of keypoint prediction in NOCS, we design a NOCS voxelization method that divides NOCS into multiple voxels and accurately predicts NOCS keypoints within these voxels. Experimental results on multiple benchmark datasets demonstrate that our proposed KeyPose method outperforms all existing methods, achieving over 20% improvement in pose accuracy on some critical datasets.
3. R²-Art: Category-Level Articulation Pose Estimation from Single RGB Image via Cascade Render Strategy
   • 分类：类别级铰接部件方法
   • 论文：https://ojs.aaai.org/index.php/AAAI/article/view/33083
   • 代码：代码未开源
   • 摘要：Human life is filled with articulated objects. Previous works for estimating the pose of category-level articulated objects rely on costly 3D point clouds or RGB-D images. In this paper, our goal is to estimate category-level articulation poses from a single RGB image, where we propose R²-Art, a novel category-level Articulation pose estimation framework from a single RGB image and a cascade Render strategy. Given an RGB image as input, R²-Art estimates per-part 6D pose for the articulation. Specifically, we design parallel regression branches tailored to generate camera-to-root translation and rotation. Using the predicted joint states, we perform PC prior transformation and deformation with a joint-centric modeling approach. For further refinement, a cascade render strategy is proposed for projecting the 3D deformed prior onto the 2D mask. Extensive experiments are provided to validate our R²-Art on various datasets ranging from synthetic datasets to real-world scenarios, demonstrating the superior performance and robustness of the R²-Art. We believe that this work has the potential to be applied in many fields including robotics, embodied intelligence, and augmented reality.
4. Universal Features Guided Zero-Shot Category-Level Object Pose Estimation
   • 分类：类别级方法
   • 论文：https://ojs.aaai.org/index.php/AAAI/article/view/32713
   • 代码：代码未开源
   • 摘要：Object pose estimation, crucial in computer vision and robotics applications, faces challenges with the diversity of unseen categories. We propose a zero-shot method to achieve category-level 6-DOF object pose estimation, which exploits both 2D and 3D universal features of input RGB-D image to establish semantic similarity-based correspondences and can be extended to unseen categories without additional model fine-tuning. Our method begins with combining efficient 2D universal features to find sparse correspondences between intra-category objects and gets initial coarse pose. To handle the correspondence degradation of 2D universal features if the pose deviates much from the target pose, we use an iterative strategy to optimize the pose. Subsequently, to resolve pose ambiguities due to shape differences between intra-category objects, the coarse pose is refined by optimizing with dense alignment constraint of 3D universal features. Our method outperforms previous methods on the REAL275 and Wild6D benchmarks for unseen categories.

SIGGRAPH

ACM MM

1. CLIP-6D: Empowering CLIP as a Zero-Shot 6D Pose Estimator Through Generalizable Object-Specific Representations
   • 分类：任意物体方法
   • 论文：https://dl.acm.org/doi/10.1145/3746027.3754497
   • 代码：代码未开源
   • 摘要：The open-vocabulary paradigm enhances 6D object pose estimation by leveraging language cues to transfer learned representations from seen to unseen objects, yet its performance suffers from a misalignment between vision-language representations and the 6D pose space. This challenge is compounded by the intrinsic lack of pose awareness in models like CLIP (Contrastive Language-Image Pre-Training). To address this limitation, we introduce CLIP-6D, which equips CLIP with the ability to estimate poses through the learning of generalizable representations specific to objects. CLIP-6D consists of three key components: (1) an innovative self-alignment strategy that enables CLIP to derive geometric representations from RGB images by leveraging its inherent feature extractor; (2) a multiplex representation interactive learning method efficiently bridges the heterogeneous representations of object category priors, geometry, and spatial correlations; (3) a lightweight adapter using knowledge distillation improves CLIP’s capture of detailed semantic representations. Experiments show that CLIP-6D achieves an improvement of 10.8% and 16.4% in the metric 5° 2 cm and 10° 5 cm for zero-shot generalization and achieves a speedup of 5.4 FPS in inference over current state-of-the-art methods. The source code and models are available at https://github.com/whoawong/CLIP-6D.git.

CCF B

ECCV

IJCAI

1. Pre-defined Keypoints Promote Category-level Articulation Pose Estimation via Multi-Modal Alignment
   • 分类：类别级铰接部件方法
   • 论文：https://www.ijcai.org/proceedings/2025/0237.pdf
   • 代码：代码未开源
   • 摘要：Articulations are essential in everyday interactions, yet traditional RGB-based pose estimation methods often struggle with issues such as lighting variations and shadows. To overcome these challenges, we propose a novel Pre-defined keypoint based framework for category-level articulation pose estimation via multi-modal AliGnMent, coined PAGE. Specifically, we first propose a customized keypoint estimation method, aiming to avoid the divergent distance pattern between heuristically generated keypoints and visible points. In addition, to reduce the mutual information redundancy between point clouds and RGB images, we design the geometry-color alignment, which fuses the features after aligning two modalities. This is followed by decoding the radius for each visible point, and applying our proposal integration scoring strategy to predict keypoints. Ultimately, the framework outputs the per-part 6D pose of the articulation. We conduct extensive experiments to evaluate PAGE across a variety of datasets, from synthetic to real-world scenarios, demonstrating its robustness and superior performance.
2. SeqPose: An End-to-End Framework to Unify Single-frame and Video-based RGB Category-Level Pose Estimation
   • 分类：类别级方法
   • 论文：https://www.ijcai.org/proceedings/2025/0137.pdf
   • 代码：代码未开源
   • 摘要：Category-level object pose estimation is a long-standing and fundamental task crucial for augmented reality and robotic manipulation applications. Existing RGB-based approaches struggle with multi-stage settings and heavily rely on off-the-shelf techniques, such as object detectors, depth estimators, non-differentiable NOCS shape alignment, etc. Extra dependencies lead to the accumulation of errors and complicate the whole pipeline, limiting the deployment of these approaches in practical applications. This paper streamlined an end-to-end framework unifying the single-frame and video-based category-level pose estimation. Specifically, instead of explicitly introducing extra dependencies, the DINOv2 encoder and depth decoder, as robust semantic and geometric prior extractors, are leveraged to produce intra-frame hierarchical semantic and geometric features. A spatial-temporal sparse query network is developed to model the implicit correspondence and inter-frame correlations between a set of implicit 3D query anchors and intra-frame features. Finally, a pose prediction head is employed using the bipartite matching algorithm. Experimental results demonstrate that our model achieves state-of-the-art performance compared with RGB-based categorical pose estimation methods on the REAL275 and CAMERA25 datasets. Our code is available at https://andrewchiyz.github.io/vision.3dv.seqpose/.

ICRA

1. A Unified End-to-End Network for Category-Level and Instance-Level Object Pose Estimation from RGB Images
   • 分类：实例级&类别级方法
   • 论文：https://ieeexplore.ieee.org/document/11128247
   • 代码：代码未开源
   • 摘要：Accurately estimating the 6-DoF pose of objects is a fundamental challenge in computer vision and robotics. While category-level pose estimation based on RGBD data has achieved good performance in recent years, estimating poses solely from RGB images remains a significant challenge. Existing RGB-based category-level methods primarily focus on recovering object point clouds from RGB images, and pose prediction is not performed end-to-end by a network. This paper presents a Category-level and Instance-level Pose Estimation Network (CIPE), which models pose estimation as a set prediction problem and enables direct pose regression from RGB images. To further enhance the network’s ability to learn object poses, first, a novel learnable rotation representation that redefines rotation learning within Euclidean space is introduced to facilitate rotation regression. Additionally, we propose a prior-query fusion strategy that utilizes a pre-trained point cloud feature extraction network to integrate categorical object features with bounding boxes, thereby improving the incorporation of category information. Experimental results demonstrate that CIPE significantly outperforms existing RGB-based methods on both category-level and instance-level datasets. The code is available at https://github.com/jialeren/CIPE.
2. MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model
   • 分类：类别级方法
   • 论文：https://ieeexplore.ieee.org/document/11127837
   • 代码：https://github.com/CNJianLiu/MonoDiff9D
   • 摘要：Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://github.com/CNJianLiu/MonoDiff9D.
3. Occlusion-Aware 6D Pose Estimation with Depth-Guided Graph Encoding and Cross-Semantic Fusion for Robotic Grasping
   • 分类：实例级方法
   • 论文：https://ieeexplore.ieee.org/document/11128248
   • 代码：代码未开源
   • 摘要：Reliable 6D pose estimation is crucial for robotic tasks but presents significant challenges in environments with occlusion. Recent approaches tend to directly predict pose parameters of object with deep neural networks, lacking the modeling ability of non-adjacent and complex relationships of surface points in occluded scenarios. To solve this problem, we propose a novel occlusion-aware 6D pose estimation framework, which uses depth-guided graph neural network (GNN) to model potential relationships from RGBD input. Two semantic information, which are mask and binary code of object, are adaptively fused to extract 2D-3D correspondence related features in an effective manner. Both enhanced graph features and fused semantic information contribute to the performance improvement of pose estimation with occlusion. Extensive experiments indicate that our approach outperforms comparative methods by 1.2% and 1.9% on LMO and YCBV datasets (up to 30% for certain objects) and its validity is also verified under real-world pose estimation test.
4. OmniShape: Zero-Shot Multi-Hypothesis Object Pose and Shape Estimation in the Real World
   • 分类：任意物体方法
   • 论文：https://ieeexplore.ieee.org/document/11128589
   • 代码：代码未开源
   • 摘要：We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first method of its kind to enable probabilistic pose and shape estimation. OmniShape is based on the key insight that shape completion can be decoupled into two multi-modal distributions: one capturing how measurements project into a normalized object reference frame defined by the dataset and the other modelling a prior over object geometries represented as triplanar neural fields. By training separate conditional diffusion models for these two distributions, we enable sampling multiple hypotheses from the joint pose and shape distribution. OmniShape demonstrates compelling performance on challenging real world datasets. Project website: https://tri-ml.github.io/omnishape.
5. You Only Estimate Once: Unified, One-stage, Real-Time Category-Level Articulated Object 6D Pose Estimation for Robotic Grasping
   • 分类：类别级铰接部件方法
   • 论文：https://ieeexplore.ieee.org/document/11128513
   • 代码：代码未开源
   • 摘要：This paper addresses the problem of category-level pose estimation for articulated objects in robotic manipulation tasks. Recent works have shown promising results in estimating part pose and size at the category level. However, these approaches primarily follow a complex multi-stage pipeline that first segments part instances in the point cloud and then estimates the Normalized Part Coordinate Space (NPCS) representation for 6D poses. These approaches suffer from high computational costs and low performance in real-time robotic tasks. To address these limitations, we propose YOEO, a single-stage method that simultaneously outputs instance segmentation and NPCS representations in an end-to-end manner. We use a unified network to generate point-wise semantic labels and centroid offsets, allowing points from the same part instance to vote for the same centroid. We further utilize a clustering algorithm to distinguish points based on their estimated centroid distances. Finally, we first separate the NPCS region of each instance. Then, we align the separated regions with the real point cloud to recover the final pose and size. Experimental results on the GAPart dataset demonstrate the pose estimation capabilities of our proposed single-shot method. We also deploy our synthetically-trained model in a real-world setting, providing real-time visual feedback at 200Hz, enabling a physical Kinova robot to interact with unseen articulated objects. This showcases the utility and effectiveness of our method.

ICME

1. Diff-Art: Category-level Articulation Pose Estimation via Conditional Diffusion
   • 分类：类别级铰接部件方法
   • 论文：https://ieeexplore.ieee.org/document/11209596
   • 代码：代码未开源
   • 摘要：Articulated objects are prevalent in people’s daily lives, yet their diverse motion structures pose significant challenges for category-level pose estimation. To address this, in this work, we introduce Diff-Art, a method tailored for category-level articulation pose estimation using conditional diffusion. Given a partial point cloud as input, Diff-Art predicts the per-part 6D pose of the articulated object. Our approach incorporates a novel modeling strategy that exploits the unique kinematic constraints of articulated objects, effectively handling self-occlusion scenarios. Furthermore, we propose a re-scoring strategy to enhance the accuracy of 6D pose estimation within the conditional diffusion framework. Extensive experiments validate the effectiveness of Diff-Art, demonstrating its strong performance on both synthetic datasets and its ability to generalize to real-world scenarios. We believe this work holds significant potential for applications in embodied AI and robotics.
2. GASEM: Boosting Generalized and Actionable Parts Segmentation and Pose Estimation via Object Motion Perception
   • 分类：类别级可操作部件方法
   • 论文：https://ieeexplore.ieee.org/document/11208907
   • 代码：https://github.com/Zirconium233/GASEM
   • 摘要：Category-level object understanding has progressed, but generalized part perception remains underexplored. This paper introduces GASEM, a framework for Generalizable and Actionable Parts GAPart Segmentation and pose Estimation via object Motion perception. GASEM utilizes point-wise motion data from observed point clouds and cross-perspective alignment to learn object motion using a scene flow model. It features a segmentation proposal architecture for GAPart segmentation and an Normalized Object Coordinate Space(NPCS) branch for pose estimation. Additionally, a reinforcement learning agent is trained for robust GAPart manipulation in both simulations and real-world environments. Experiments on the GAPartNet dataset show GASEM outperforms state-of-the-art methods. This work promises advancements in embodied intelligence applications like robot-object interaction and generalizable manipulation. Codes are available at https://github.com/Zirconium233/GASEM.
3. Multi-mode Bidirectional Feature Fusion and Domain-consistency Refinement for Real-time Monocular 6D Object Pose Estimation
   • 分类：实例级方法
   • 论文：https://ieeexplore.ieee.org/document/11208924
   • 代码：代码未开源
   • 摘要：Current pose estimation methods are confronted computational performance, domain gap in refinement and feature sharing among various semantic tasks. To address these challenges, we present a lightweight pose estimation framework with the following three novel contributions. Firstly, we propose a novel for real-time object pose estimation, composed of the multi-task network called DSPNet to exploit semantic relations among different tasks and 3D Gaussian based pose refinement to address the domain gap problem. Secondly, the both networks learn temporal features of previous camera trajectory by a transformer based module to further improve estimation robustness. Thirdly, we design a bidirectional fusion module between consecutive decoding layers in DSPNet to achieve two-sided feature sharing including various semantic features and spatio-temporal features. Experimental results on the public LineMod, LineMod-Occluded and YCB-Video datasets demonstrate the state-of-the-art performance in comparison with other RGB based methods while keeping 37 FPS on an Nvidia GTX 3090 GPU.

ECAI

ISMAR

CCF C

AISTATS

IROS

1. 6-DoF Object Tracking with Event-based Optical Flow and Frames
   • 分类：实例级跟踪方法
   • 论文：https://ieeexplore.ieee.org/document/11245865
   • 代码：代码未开源
   • 摘要：Tracking the position and orientation of objects in space (i.e., in 6-DoF) in real time is a fundamental problem in robotics for environment interaction. It becomes more challenging when objects move at high-speed due to frame rate limitations in conventional cameras and motion blur. Event cameras are characterized by high temporal resolution, low latency and high dynamic range, that can potentially overcome the impacts of motion blur. Traditional RGB cameras provide rich visual information that is more suitable for the challenging task of single-shot object pose estimation. In this work, we propose using event-based optical flow combined with an RGB based global object pose estimator for 6-DoF pose tracking of objects at high-speed, exploiting the core advantages of both types of vision sensors. Specifically, we propose an event-based optical flow algorithm for object motion measurement to implement an object 6-DoF velocity tracker. By integrating the tracked object 6-DoF velocity with low frequency estimated pose from the global pose estimator, the method can track pose when objects move at high-speed. The proposed algorithm is tested and validated on both synthetic and real world data, demonstrating its effectiveness, especially in high-speed motion scenarios.
2. Distilling 3D distinctive local descriptors for 6D pose estimation
   • 分类：任意物体方法
   • 论文：https://ieeexplore.ieee.org/document/11247352
   • 代码：https://github.com/tev-fbk/dGeDi/
   • 摘要：Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptors, GeDi has demonstrated strong zero-shot 6D pose estimation capabilities but remains computationally impractical for real-world applications due to its expensive inference process. Can we retain GeDi’s effectiveness while significantly improving its efficiency? In this paper, we explore this question by introducing a knowledge distillation framework that trains an efficient student model to regress local descriptors from a GeDi teacher. Our key contributions include: an efficient large-scale training procedure that ensures robustness to occlusions and partial observations while operating under compute and storage constraints, and a novel loss formulation that handles weak supervision from non-distinctive teacher descriptors. We validate our approach on five BOP Benchmark datasets and demonstrate a significant reduction in inference time while maintaining competitive performance with existing methods, bringing zero-shot 6D pose estimation closer to real-time feasibility. Project Website: https://tev-fbk.github.io/dGeDi/.
3. DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
   • 分类：任意物体跟踪方法
   • 论文：https://ieeexplore.ieee.org/document/11247403
   • 代码：https://github.com/Robotics-STAR-Lab/DynamicPose
   • 摘要：We present DynamicPose, a retraining-free 6D pose tracking framework that improves tracking robustness in fast-moving camera and object scenarios. Previous work is mainly applicable to static or quasi-static scenes, and its performance significantly deteriorates when both the object and the camera move rapidly. To overcome these challenges, we propose three synergistic components: (1) A visual-inertial odometry compensates for the shift in the Region of Interest (ROI) caused by camera motion; (2) A depth-informed 2D tracker corrects ROI deviations caused by large object translation; (3) A VIO-guided Kalman filter predicts object rotation, generates multiple candidate poses, and then obtains the final pose by hierarchical refinement. The 6D pose tracking results guide subsequent 2D tracking and Kalman filter updates, forming a closed-loop system that ensures accurate pose initialization and precise pose tracking. Simulation and real-world experiments demonstrate the effectiveness of our method, achieving real-time and robust 6D pose tracking for fast-moving cameras and objects.
4. Generalizable and Actionable Part Detection and Manipulation with SAM-rectified Segmentation and Iterative Pose Refinement
   • 分类：类别级可操作部件方法
   • 论文：https://ieeexplore.ieee.org/document/11246094
   • 代码：代码未开源
   • 摘要：The ability to perform cross-category object perception and manipulation is highly desirable in building intelligent robots. One promising approach is to define the concept of Generalizable and Actionable Parts (GAParts), such as buttons and handles, on both seen and unseen object categories. However, the accurate cross-category perception of GAParts is still challenging due to the large inter-category object shape variations. To address this issue, we introduce SAMIR, a novel framework using SAM-rectified segmentation and Iterative pose Refinement for GAPart detection and manipulation. Firstly, we introduce a Segment Anything (SAM) segmentation prior to rectify the unconfident, fragmented GAPart instance proposals. Secondly, in addition to the zero-shot generalization of the SAM foundation model, we further finetune it with a lightweight adaptor model on our task dataset. Finally, we propose an iterative pose refinement procedure that improves the accuracy of GAPart pose estimation. Our perception experiments on GAPartNet dataset show that SAMIR consistently outperforms the baseline method on instance segmentation and pose estimation tasks. Our manipulation experiments in Sapien simulator illustrate that SAMIR leads to an improved manipulation success rate. We also deploy our method to a real robot for real-world manipulation. Our code and video are available at sites.google.com/view/samir-gapart.
5. Improving 6D Object Pose Estimation of Metallic Household and Industry Objects
   • 分类：实例级方法
   • 论文：https://ieeexplore.ieee.org/document/11247314
   • 代码：代码未开源
   • 摘要：6D object pose estimation suffers from reduced accuracy when applied to metallic objects. We set out to improve the state-of-the-art by addressing challenges such as reflections and specular highlights in industrial applications. Our novel BOP-compatible dataset, featuring a diverse set of metallic objects (cans, household, and industrial items) under various lighting and background conditions, provides additional geometric and visual cues. We demonstrate that these cues can be effectively leveraged to enhance overall performance. To illustrate the usefulness of the additional features, we improve upon the GDRNPP algorithm by introducing an additional keypoint prediction and material estimator head in order to improve spatial scene understanding. Evaluations on the new dataset show improved accuracy for metallic objects, supporting the hypothesis that additional geometric and visual cues can improve learning.
6. MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint Learning
   • 分类：类别级方法
   • 论文：https://ieeexplore.ieee.org/document/11247325
   • 代码：https://github.com/yangyifanYYF/MK-Pose
   • 摘要：Category-level object pose estimation, which predicts the pose of objects within a known category without prior knowledge of individual instances, is essential in applications like warehouse automation and manufacturing. Existing methods relying on RGB images or point cloud data often struggle with object occlusion and generalization across different instances and categories. This paper proposes a multimodal-based keypoint learning framework (MK-Pose) that integrates RGB images, point clouds, and category-level textual descriptions. The model uses a self-supervised keypoint detection module enhanced with attention-based query generation, soft heatmap matching and graph-based relational modeling. Additionally, a graph-enhanced feature fusion module is designed to integrate local geometric information and global context. MK-Pose is evaluated on CAMERA25 and REAL275 dataset, and is further tested for cross-dataset capability on HouseCat6D dataset. The results demonstrate that MK-Pose outperforms existing state-of-the-art methods in both IoU and average precision without shape priors. Codes will be released at https://github.com/yangyifanYYF/MK-Pose.
7. Occlusion-Aware 6D Pose Estimation with Visual Observation Guided Diffusion Model
   • 分类：类别级方法
   • 论文：https://ieeexplore.ieee.org/document/11247112
   • 代码：代码未开源
   • 摘要：Category-level 6D pose estimation in cluttered and occluded environments is a challenging task. Most existing methods rely on deterministic point-based correspondences to estimate target poses, which cannot consider the uncertainty for occluded objects, and thus result in inferior performance. In this paper, we propose a diffusion model guided by occlusion-aware observations to adaptively refine the object poses in occluded and cluttered scenes. Specifically, we first extract various 2D and 3D features from an RGB-D image to construct the conditions of diffusion model. In the reverse diffusion process, the model is guided by implicit correspondences, perception distance, and occlusion relationships to refine the noisy pose sampled from a standard Gaussian distribution. With several denoising steps, our method can produce accurate results that are consistent with image observations in occluded scenarios. The experimental results show that the proposed method can outperform baseline methods in major metrics in occlusion scenarios. Furthermore, our approach can also be applied in robotic grasping and manipulation tasks through grasping experiments in a cluttered enviroment on a physical UR5 robot.
8. OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
   • 分类：任意物体跟踪方法
   • 论文：https://ieeexplore.ieee.org/document/11246949
   • 代码：代码未开源
   • 摘要：To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes.
9. RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base
   • 分类：任意物体方法
   • 论文：https://ieeexplore.ieee.org/document/11246449
   • 代码：https://github.com/SresserS/RAG-6DPose-code
   • 摘要：Accurate 6D pose estimation is key for robotic manipulation, enabling precise object localization for tasks like grasping. We present RAG-6DPose, a retrieval-augmented approach that leverages 3D CAD models as a knowledge base by integrating both visual and geometric cues. Our RAG-6DPose roughly contains three stages: 1) Building a Multi-Modal CAD Knowledge Base by extracting 2D visual features from multi-view CAD rendered images and also attaching 3D points; 2) Retrieving relevant CAD features from the knowledge base based on the current query image via our ReSPC module; and 3) Incorporating retrieved CAD information to refine pose predictions via retrieval-augmented decoding. Experimental results on standard benchmarks and real-world robotic tasks demonstrate the effectiveness and robustness of our approach, particularly in handling occlusions and novel viewpoints. Supplementary material is available on our project website: https://sressers.github.io/RAG-6DPose.
10. RCGNet: RGB-based Category-Level 6D Object Pose Estimation with Geometric Guidance
    • 分类：类别级方法
    • 论文：https://ieeexplore.ieee.org/document/11247442
    • 代码：代码未开源
    • 摘要：While most current RGB-D-based category-level object pose estimation methods achieve strong performance, they face significant challenges in scenes lacking depth information. In this paper, we propose a novel category-level object pose estimation approach that relies solely on RGB images. This method enables accurate pose estimation in real-world scenarios without the need for depth data. Specifically, we design a transformer-based neural network for category-level object pose estimation, where the transformer is employed to predict and fuse the geometric features of the target object. To ensure that these predicted geometric features faithfully capture the object’s geometry, we introduce a geometric feature-guided algorithm, which enhances the network’s ability to effectively represent the object’s geometric information. Finally, we utilize the RANSAC-PnP algorithm to compute the object’s pose, addressing the challenges associated with variable object scales in pose estimation. Experimental results on benchmark datasets demonstrate that our approach is not only highly efficient but also achieves superior accuracy compared to previous RGB-based methods. These promising results offer a new perspective for advancing category-level object pose estimation using RGB images.
11. RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
    • 分类：任意物体跟踪方法
    • 论文：https://ieeexplore.ieee.org/document/11247381
    • 代码：https://github.com/GreatenAnoymous/RGBTrack
    • 摘要：We introduce a robust framework, RGBTrack, for real-time 6D pose estimation and tracking that operates solely on RGB data, thereby eliminating the need for depth input for such dynamic and precise object pose tracking tasks. Building on the FoundationPose architecture, we devise a novel binary search strategy combined with a render-and-compare mechanism to efficiently infer depth and generate robust pose hypotheses from true-scale CAD models. To maintain stable tracking in dynamic scenarios, including rapid movements and occlusions, RGBTrack integrates state-of-the-art 2D object tracking (XMem) with a Kalman filter and a state machine for proactive object pose recovery. In addition, RGBTrack’s scale recovery module dynamically adapts CAD models of unknown scale using an initial depth estimate, enabling seamless integration with modern generative reconstruction techniques. Extensive evaluations on benchmark datasets demonstrate that RGBTrack’s novel depth-free approach achieves competitive accuracy and real-time performance, making it a promising practical solution candidate for application areas including robotics, augmented reality, and computer vision. The source code for our implementation will be made publicly available at https://github.com/GreatenAnoymous/RGBTrack.git.
12. Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation
    • 分类：实例级方法
    • 论文：https://ieeexplore.ieee.org/document/11245982
    • 代码：代码未开源
    • 摘要：Compact and efficient 6DoF object pose estimation is crucial in applications such as robotics, augmented reality, and space autonomous navigation systems, where lightweight models are critical for real-time accurate performance. This paper introduces a novel uncertainty-aware end-to-end Knowledge Distillation (KD) framework focused on keypoint-based 6DoF pose estimation. Keypoints predicted by a large teacher model exhibit varying levels of uncertainty that can be exploited within the distillation process to enhance the accuracy of the student model while ensuring its compactness. To this end, we propose a distillation strategy that aligns the student and teacher predictions by adjusting the knowledge transfer based on the uncertainty associated with each teacher keypoint prediction. Additionally, the proposed KD leverages this uncertainty-aware alignment of keypoints to transfer the knowledge at key locations of their respective feature maps. Experiments on the widely-used LINEMOD benchmark demonstrate the effectiveness of our method, achieving superior 6DoF object pose estimation with lightweight models compared to state-of-the-art approaches. Further validation on the SPEED+ dataset for spacecraft pose estimation highlights the robustness of our approach under diverse 6DoF pose estimation scenarios.
13. VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation
    • 分类：实例级方法
    • 论文：https://ieeexplore.ieee.org/document/11247012
    • 代码：https://github.com/RuyiLian/VAPO
    • 摘要：Localizing predefined 3D keypoints in a 2D image is an effective way to establish 3D-2D correspondences for 6DoF object pose estimation. However, unreliable localization results of invisible keypoints degrade the quality of correspondences. In this paper, we address this issue by localizing the important keypoints in terms of visibility. Since keypoint visibility information is currently missing in the dataset collection process, we propose an efficient way to generate binary visibility labels from available object-level annotations, for keypoints of both asymmetric objects and symmetric objects. We further derive real-valued visibility-aware importance from binary labels based on the PageRank algorithm. Taking advantage of the flexibility of our visibility-aware importance, we construct VAPO (Visibility-Aware POse estimator) by integrating the visibility-aware importance with a state-of-the-art pose estimation algorithm, along with additional positional encoding. VAPO can work in both CAD-based and CAD-free settings. Extensive experiments are conducted on popular pose estimation benchmarks including Linemod, Linemod-Occlusion, and YCB-V. The results show that, VAPO significantly improves both the keypoint correspondences and final estimated poses, and clearly achieves state-of-the-art performances.

ICPR

IJCNN

BMVC

3DV

1. GS-Pose: Generalizable Segmentation-Based 6D Object Pose Estimation With 3D Gaussian Splatting
   • 分类：任意物体方法
   • 论文：https://openreview.net/pdf?id=8xHWWXZ9pq
   • 代码：https://github.com/dingdingcai/GSPose
   • 摘要：This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen object and builds three distinct representations stored in a database. At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method. The key insight is the application of the appropriate object representation at each stage of the process. In particular, for the refinement step, we leverage 3D Gaussian splatting, a novel differentiable rendering technique that offers high rendering speed and relatively low optimization time. Off-the-shelf toolchains and commodity hardware, such as mobile phones, can be used to capture new objects to be added to the database. Extensive evaluations on the LINEMOD and OnePose-LowTexture datasets demonstrate excellent performance, establishing the new state-of-the-art. The source code is publicly available at https://github.com/dingdingcai/GSPose.

ICIP

1. Adapting Foundation Features via Cross-View Contrastive Learning for Unseen Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://ieeexplore.ieee.org/document/11084358
   • 代码：代码未开源
   • 摘要：Recent advances in visual foundation models, such as DINOv2, have shown impressive generalization capabilities, making them a strong baseline for complex tasks like unseen object pose estimation. However, a significant domain gap between real images and rendered templates, combined with limited 3D awareness, hinders their effectiveness in precise correspondence matching and pose estimation. In this paper, we propose a new adaptation method that reduces this domain gap and enhances multi-view consistency through cross-view contrastive learning. Our approach aligns features from real and rendered images by using sampled poses and 2D-2D correspondences while also leveraging advanced augmentations and PCA-based loss computation to improve robustness and efficiency. When applied to the FoundPose model, our method achieves state-of-the-art performance on the BOP benchmark, surpassing previous methods with minimal fine-tuning.

ACCV

ICANN

1. RDGE-6D: Reverse Direction Geometry Injection for 6D Pose Estimation
   • 分类：实例级方法
   • 论文：https://link.springer.com/chapter/10.1007/978-3-032-04546-1_25
   • 代码：代码未开源
   • 摘要：The use of RGBD images for obtaining high-accuracy 6D pose estimation results has gained popularity due to the availability of inexpensive RGB-D sensors. Typically, state-of-the-art methods employ deep learning-based methods to process RGBD data for pose estimation. They either use different backbones to extract features from each type of image or combine preprocessed depth images with RGB images before feeding them into a network. However, with increased network layers, deep networks tend to lose detail, which may be crucial for object localization. Additionally, the influence of occlusion also becomes more pronounced, posing challenges in effectively focusing on target objects. To address these issues, we propose RDGE-6D, an intuitive and effective method that leverages reverse direction fusion to enhance RGB deep features using shallow features from depth images. Extensive experiments on benchmark datasets demonstrate the superior performance of our method, especially in scenarios with heavy occlusion.

ACML

其他

WACV

1. Generalizable Single-View Object Pose Estimation by Two-Side Generating and Matching
   • 分类：任意物体方法
   • 论文：https://openaccess.thecvf.com/content/WACV2025/html/Sun_Generalizable_Single-View_Object_Pose_Estimation_by_Two-Side_Generating_and_Matching_WACV_2025_paper.html
   • 代码：https://github.com/scy639/Gen2SM
   • 摘要：In this paper, we present a novel generalizable object pose estimation method to determine the object pose using only one RGB image. Unlike traditional approaches that rely on instance-level object pose estimation and necessitate extensive training data, our method offers generalization to unseen objects without extensive training, operates with a single reference image of the object, and eliminates the need for 3D object models or multiple views of the object. These characteristics are achieved by utilizing a diffusion model to generate novel-view images and conducting a two-sided matching on these generated images. Quantitative experiments demonstrate the superiority of our method over existing pose estimation techniques across both synthetic and real-world datasets. Remarkably, our approach maintains strong performance even in scenarios with significant viewpoint changes, highlighting its robustness and versatility in challenging conditions. The code will be released at https://github.com/scy639/Gen2SM.
2. Shape-Biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose Estimation
   • 分类：实例级方法
   • 论文：https://openaccess.thecvf.com/content/WACV2025/html/Honig_Shape-Biased_Texture_Agnostic_Representations_for_Improved_Textureless_and_Metallic_Object_WACV_2025_paper.html
   • 代码：https://github.com/hoenigpeter/randomized_texturing
   • 摘要：Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due to few visual cues and the texture bias of CNNs. To address this issue, we propose a strategy for inducing a shape bias to CNN training. In particular, by randomizing textures applied to object surfaces during data rendering, we create training data without consistent texture cues. This methodology allows for seamless integration into existing data rendering engines, and results in negligible computational overhead for data rendering and network training. Our findings demonstrate that the shape bias we induce via randomized texturing, improves over existing approaches using style transfer. We evaluate with five detectors and two pose estimators. For three object detectors and for pose estimation in general, estimation accuracy improves for textureless and metallic objects. Additionally we show that our approach increases the pose estimation accuracy in the presence of image noise and strong illumination changes. Code available at https://github.com/hoenigpeter/randomized_texturing.

CoRL

1. One View, Many Worlds: Single-Image to 3D object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openreview.net/pdf?id=kto4zVmo4w
   • 代码：https://github.com/gzwsama/OnePoseviaGen
   • 摘要：Estimating the 6D pose of arbitrary objects from a single reference image is a critical yet challenging task in robotics, especially considering the long-tail distribution of real-world instances. While category-level and model-based approaches have achieved notable progress, they remain limited in generalizing to unseen objects under one-shot settings. In this work, we propose a novel pipeline for fast and accurate one-shot 6D pose and scale estimation. Leveraging recent advances in single-view 3D generation, we first build high-fidelity textured meshes without requiring known object poses. To resolve scale ambiguity, we introduce a coarse-to-fine alignment module that estimates both object size and initial pose by matching 2D-3D features with depth information. We then generate a diversified set of plausible 3D models using text-guided generative augmentation and render them with Blender to synthesize large-scale, domain-randomized training data for pose estiamtion. This synthetic data bridges the domain gap and enables robust fine-tuning of pose estimators. Our method achieves state-of-the-art results on several 6D pose benchmarks, and we further validate its effectiveness on a newly collected in-the-wild dataset. Finally, we integrate our system with a dexterous hand, demonstrating its robustness in real-world robotic grasping tasks. All code, data, and models will be released to foster future research.
2. PicoPose: Progressive Pixel-to-Pixel Correspondence Learning for Novel Object Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openreview.net/pdf?id=EXgckdYESp
   • 代码：https://github.com/foollh/PicoPose
   • 摘要：RGB-based novel object pose estimation is critical for rapid deployment in robotic applications, yet zero-shot generalization remains a key challenge. In this paper, we introduce PicoPose, a novel framework designed to tackle this task using a three-stage pixel-to-pixel correspondence learning process. Firstly, PicoPose matches features from the RGB observation with those from rendered object templates, identifying the best-matched template and establishing coarse correspondences. Secondly, PicoPose smooths the correspondences by globally regressing a 2D affine transformation, including in-plane rotation, scale, and 2D translation, from the coarse correspondence map. Thirdly, PicoPose applies the affine transformation to the feature map of the best-matched template and learns correspondence offsets within local regions to achieve fine-grained correspondences. By progressively refining the correspondences, PicoPose significantly improves the accuracy of object poses computed via PnP/RANSAC. PicoPose achieves state-of-the-art performance on the seven core datasets of the BOP benchmark, demonstrating exceptional generalization to novel objects. Code and trained models are available at https://github.com/foollh/PicoPose.
3. UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation
   • 分类：任意物体方法
   • 论文：https://openreview.net/pdf?id=L5PJBd8ahD
   • 代码：代码未开源
   • 摘要：Estimating the 6D pose of novel objects is a fundamental yet challenging problem in robotics, often relying on access to object CAD models. However, acquiring such models can be costly and impractical. Recent approaches aim to bypass this requirement by leveraging strong priors from foundation models to reconstruct objects from single or multi-view images, but typically require additional training or produce hallucinated geometry. To this end, we propose UnPose, a novel framework for zero-shot, model-free 6D object pose estimation and reconstruction that exploits 3D priors and uncertainty estimates from a pre-trained diffusion model. Specifically, starting from a single-view RGB-D frame, UnPose uses a multi-view diffusion model to estimate an initial 3D model using 3D Gaussian Splatting (3DGS) representation, along with pixel-wise epistemic uncertainty estimates. As additional observations become available, we incrementally refine the 3DGS model by fusing new views guided by the diffusion model’s uncertainty, thereby, continuously improving the pose estimation accuracy and 3D reconstruction quality. To ensure global consistency, the diffusion prior-generated views and subsequent observations are further integrated in a pose graph and jointly optimized into a coherent 3DGS field. Extensive experiments demonstrate that UnPose significantly outperforms existing approaches in both 6D pose estimation accuracy and 3D reconstruction quality. We further showcase its practical applicability in real-world robotic manipulation tasks. Video demos can be found at our project page: https://frankzhaodong.github.io/UnPose.

RSS

1. V-HOP: Visuo-Haptic 6D Object Pose Tracking
   • 分类：任意物体跟踪方法
   • 论文：https://roboticsconference.org/2025/program/papers/37/
   • 代码：https://github.com/brown-ivl/v-hop
   • 摘要：Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this multisensory integration, prior object pose estimation research has attempted to combine visual and haptic/tactile feedback. Although these works demonstrate improvements in controlled environments or synthetic datasets, they often underperform vision-only approaches in real-world settings due to poor generalization across diverse grippers, sensor layouts, or sim-to-real environments. Furthermore, they typically estimate the object pose for each frame independently, resulting in less coherent tracking over sequences in real-world deployments. To address these limitations, we introduce a novel unified haptic representation that effectively handles multiple gripper embodiments. Building on this representation, we introduce a new visuo-haptic transformer-based object pose tracker that seamlessly integrates visual and haptic input. We validate our framework in our dataset and the Feelsight dataset, demonstrating significant performance improvement on challenging sequences. Notably, our method achieves superior generalization and robustness across novel embodiments, objects, and sensor types (both tactile-based and vision-based tactile sensors). In real-world experiments, we demonstrate that our approach outperforms state-of-the-art visual trackers by a large margin. We further show that we can achieve precise manipulation tasks by incorporating our real-time object tracking result into motion plans, underscoring the advantages of visuo-haptic perception. Project website: https://ivl.cs.brown.edu/research/v-hop.

SIGGRAPH Asia