Structure-Aware Correspondence Learning for Relative Pose Estimation

• URL: http://arxiv.org/abs/2503.18671v1
• Date: Mon, 24 Mar 2025 13:43:44 GMT
• Title: Structure-Aware Correspondence Learning for Relative Pose Estimation
• Authors: Yihan Chen, Wenfei Yang, Huan Ren, Shifeng Zhang, Tianzhu Zhang, Feng Wu,
• Abstract summary: Relative pose estimation provides a promising way for achieving object-agnostic pose estimation.<n>Existing 3D correspondence-based methods suffer from small overlaps in visible regions and unreliable feature estimation for invisible regions.<n>We propose a novel Structure-Aware Correspondence Learning method for Relative Pose Estimation, which consists of two key modules.
• Score: 65.44234975976451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 kepoints 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{\deg}reduction in mean angular error on the CO3D dataset.

Related papers

• Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image [52.11275397911693]
  We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image. We depart from previous works that rely on learning instance-level latent space, focusing on man-made articulated objects with predefined part counts. Our method successfully reconstructs variously structured multiple instances that previous works cannot handle, and outperforms prior works in shape reconstruction and kinematics estimation.
  arXiv  Detail & Related papers  (2025-04-04T05:08:04Z)
• Learning Shape-Independent Transformation via Spherical Representations for Category-Level Object Pose Estimation [42.48001557547222]
  Category-level object pose estimation aims to determine the pose and size of novel objects in specific categories.<n>Existing correspondence-based approaches typically adopt point-based representations to establish the correspondences between primitive observed points and normalized object coordinates.<n>We introduce a novel architecture called SpherePose, which yields precise correspondence prediction through three core designs.
  arXiv  Detail & Related papers  (2025-03-18T05:43:42Z)
• Correspondence-Free Pose Estimation with Patterns: A Unified Approach for Multi-Dimensional Vision [10.274601503572715]
  A new correspondence-free pose estimation method and its practical algorithms are proposed.<n>By taking the considered point sets as patterns, feature functions used to describe these patterns are introduced to establish a sufficient number of equations for optimization.<n>The proposed method is applicable to nonlinear transformations such as perspective projection and can cover various pose estimations from 3D-to-3D points, 3D-to-2D points, and 2D-to-2D points.
  arXiv  Detail & Related papers  (2025-02-26T14:38:44Z)
• 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)
• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network [66.24726878647543]
  Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based solutions. We propose a novel pose estimation algorithm named CheckerPose, which improves on three main aspects.
  arXiv  Detail & Related papers  (2023-03-29T17:30:53Z)
• Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance [33.10167928198986]
  Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category. We present a novel self-supervised strategy that solves this problem without any human labels.
  arXiv  Detail & Related papers  (2023-02-28T03:02:11Z)
• LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation [69.70498875887611]
  LocPoseNet is able to robustly learn location prior for unseen objects. Our method outperforms existing works by a large margin on LINEMOD and GenMOP.
  arXiv  Detail & Related papers  (2022-11-29T15:21:34Z)
• DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation [43.963630959349885]
  We introduce a new method of Deep Correspondence Learning Network for direct 6D object pose estimation, shortened as DCL-Net. We show that DCL-Net outperforms existing methods on three benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD.
  arXiv  Detail & Related papers  (2022-10-11T08:04:40Z)
• What's in your hands? 3D Reconstruction of Generic Objects in Hands [49.12461675219253]
  Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic hand-held object without knowing their 3D templates.
  arXiv  Detail & Related papers  (2022-04-14T17:59:02Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)

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.