SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation

• URL: http://arxiv.org/abs/2311.11125v3
• Date: Fri, 22 Mar 2024 00:36:02 GMT
• Title: SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
• Authors: Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, Benjamin Busam,
• Abstract summary: Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. We present SecondPose, a novel approach integrating object-specific geometric features with semantic category priors from DINOv2.
• Score: 79.12683101131368
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. Existing works utilizing mean shapes often fall short of capturing this variation. To address this issue, we present SecondPose, a novel approach integrating object-specific geometric features with semantic category priors from DINOv2. Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information. These geometric features are then point-aligned with DINOv2 features to establish a consistent object representation under SE(3) transformations, facilitating the mapping from camera space to the pre-defined canonical space, thus further enhancing pose estimation. Extensive experiments on NOCS-REAL275 demonstrate that SecondPose achieves a 12.4% leap forward over the state-of-the-art. Moreover, on a more complex dataset HouseCat6D which provides photometrically challenging objects, SecondPose still surpasses other competitors by a large margin.

Related papers

• Universal Features Guided Zero-Shot Category-Level Object Pose Estimation [52.29006019352873]
  We propose a zero-shot method to achieve category-level 6-DOF object pose estimation. Our method exploits both 2D and 3D universal features of input RGB-D image to establish semantic similarity-based correspondences. Our method outperforms previous methods on the REAL275 and Wild6D benchmarks for unseen categories.
  arXiv  Detail & Related papers  (2025-01-06T08:10:13Z)
• RelPose++: Recovering 6D Poses from Sparse-view Observations [66.6922660401558]
  We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images) We build on the recent RelPose framework which learns a network that infers distributions over relative rotations over image pairs. Our final system results in large improvements in 6D pose prediction over prior art on both seen and unseen object categories.
  arXiv  Detail & Related papers  (2023-05-08T17:59:58Z)
• Generative Category-Level Shape and Pose Estimation with Semantic Primitives [27.692997522812615]
  We propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space. We show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset.
  arXiv  Detail & Related papers  (2022-10-03T17:51:54Z)
• 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)
• CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds [97.63549045541296]
  We propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances and per-part pose tracking for articulated objects. Our method achieves new state-of-the-art performance on category-level rigid object pose (NOCS-REAL275) and articulated object pose benchmarks (SAPIEN, BMVC) at the fastest FPS 12.
  arXiv  Detail & Related papers  (2021-04-08T00:14:58Z)
• FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism [49.89268018642999]
  We propose a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation. The proposed method achieves state-of-the-art performance in both category- and instance-level 6D object pose estimation.
  arXiv  Detail & Related papers  (2021-03-12T03:07:24Z)

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.