GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation

• URL: http://arxiv.org/abs/2503.15110v2
• Date: Thu, 20 Mar 2025 10:15:48 GMT
• Title: GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation
• Authors: Zinqin Huang, Gu Wang, Chenyangguang Zhang, Ruida Zhang, Xiu Li, Xiangyang Ji,
• Abstract summary: We introduce GIVEPose, a framework that implements Gradual Intra-class Variation Elimination for category-level object pose estimation.<n>GIVEPose significantly outperforms existing state-of-the-art RGB-based approaches.
• Score: 61.46277064819665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation [52.910282443646864]
  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.<n>We present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior.
  arXiv  Detail & Related papers  (2025-02-06T18:35:13Z)
• UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image [86.7128543480229]
  Unseen object pose estimation methods often rely on CAD models or multiple reference views.<n>To simplify reference acquisition, we aim to estimate the unseen object's pose through a single unposed RGB-D reference image.<n>We present a novel approach and benchmark, termed UNOPose, for unseen one-reference-based object pose estimation.
  arXiv  Detail & Related papers  (2024-11-25T05:36:00Z)
• LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation [43.549593231397644]
  LaPose is a novel framework that models the object shape as the Laplacian mixture model for Pose estimation. By representing each point as a probabilistic distribution, we explicitly quantify the shape uncertainty. LaPose yields state-of-the-art performance in category-level object pose estimation.
  arXiv  Detail & Related papers  (2024-09-24T04:20:18Z)
• RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery [72.13154206106259]
  We propose a novel pipeline that decouples the 6D pose and size estimation to mitigate the influence of imperfect scales on rigid transformations. Specifically, we leverage a pre-trained monocular estimator to extract local geometric information. A separate branch is designed to directly recover the metric scale of the object based on category-level statistics.
  arXiv  Detail & Related papers  (2023-09-19T02:20:26Z)
• CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement [52.41884119329864]
  Category-level object pose and size refiner CATRE is able to iteratively enhance pose estimate from point clouds to produce accurate results. Our approach remarkably outperforms state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed of 85.32Hz.
  arXiv  Detail & Related papers  (2022-07-17T05:55:00Z)
• GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting [103.74918834553249]
  GPV-Pose is a novel framework for robust category-level pose estimation. It harnesses geometric insights to enhance the learning of category-level pose-sensitive features. It produces superior results to state-of-the-art competitors on common public benchmarks.
  arXiv  Detail & Related papers  (2022-03-15T13:58:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.