CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement

• URL: http://arxiv.org/abs/2207.08082v1
• Date: Sun, 17 Jul 2022 05:55:00 GMT
• Title: CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
• Authors: Xingyu Liu, Gu Wang, Yi Li, Xiangyang Ji
• Abstract summary: 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.
• Score: 52.41884119329864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc. Orthogonal to them, this work presents a category-level object pose and size refiner CATRE, which is able to iteratively enhance pose estimate from point clouds to produce accurate results. Given an initial pose estimate, CATRE predicts a relative transformation between the initial pose and ground truth by means of aligning the partially observed point cloud and an abstract shape prior. In specific, we propose a novel disentangled architecture being aware of the inherent distinctions between rotation and translation/size estimation. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on REAL275, CAMERA25, and LM benchmarks up to a speed of ~85.32Hz, and achieves competitive results on category-level tracking. We further demonstrate that CATRE can perform pose refinement on unseen category. Code and trained models are available.

Related papers

• UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image [86.7128543480229]
  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. We recalibrate the weight of each correspondence based on its predicted likelihood of being within the overlapping region.
  arXiv  Detail & Related papers  (2024-11-25T05:36:00Z)
• DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation [9.074911955402495]
  Category-level object pose estimation involves estimating the 6D pose and the 3D metric size of objects from predetermined categories. Recent approaches take categorical shape prior information as reference to improve pose estimation accuracy. We propose a two-stage deformation-and registration pipeline called DR-Pose, which consists of completion-aided deformation stage and scaled registration stage.
  arXiv  Detail & Related papers  (2023-09-05T03:24:09Z)
• GenPose: Generative Category-level Object Pose Estimation via Diffusion Models [5.1998359768382905]
  We propose a novel solution by reframing categorylevel object pose estimation as conditional generative modeling. Our approach achieves state-of-the-art performance on the REAL275 dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics.
  arXiv  Detail & Related papers  (2023-06-18T11:45:42Z)
• TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation [86.80589902825196]
  We propose a method of test-time adaptation for category-level object pose estimation called TTA-COPE. We design a pose ensemble approach with a self-training loss using pose-aware confidence. Our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
  arXiv  Detail & Related papers  (2023-03-29T14:34:54Z)
• SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation [77.88624073105768]
  Category-level pose estimation is a challenging problem due to intra-class shape variations. We propose an end-to-end trainable network SSP-Pose for category-level pose estimation. SSP-Pose produces superior performance compared with competitors with a real-time inference speed at about 25Hz.
  arXiv  Detail & Related papers  (2022-08-13T14:37:31Z)
• RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation [103.74918834553247]
  Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories. Recent methods harness shape prior adaptation to map the observed point cloud into the canonical space and apply Umeyama algorithm to recover the pose and size. We propose a novel geometry-guided Residual Object Bounding Box Projection network RBP-Pose that jointly predicts object pose and residual vectors.
  arXiv  Detail & Related papers  (2022-07-30T14:45:20Z)
• Towards Self-Supervised Category-Level Object Pose and Size Estimation [121.28537953301951]
  This work presents a self-supervised framework for category-level object pose and size estimation from a single depth image. We leverage the geometric consistency residing in point clouds of the same shape for self-supervision.
  arXiv  Detail & Related papers  (2022-03-06T06:02:30Z)
• Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation [30.04752448942084]
  Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models. We propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.
  arXiv  Detail & Related papers  (2021-10-30T06:46:44Z)

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.