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Category-Agnostic Pose Estimation for Point Clouds (2403.07437v1)

Published 12 Mar 2024 in cs.CV

Abstract: The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input. Unfortunately, when faced with new categories, both instance-based and category-based methods are unable to deal with unseen objects of unseen categories, which is a challenge for pose estimation. To address this issue, this paper proposes a method to introduce geometric features for pose estimation of point clouds without requiring category information. The method is based only on the patch feature of the point cloud, a geometric feature with rotation invariance. After training without category information, our method achieves as good results as other category-based methods. Our method successfully achieved pose annotation of no category information instances on the CAMERA25 dataset and ModelNet40 dataset.

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Authors (5)
  1. Bowen Liu (63 papers)
  2. Wei Liu (1136 papers)
  3. Siang Chen (10 papers)
  4. Pengwei Xie (53 papers)
  5. Guijin Wang (23 papers)
Citations (1)
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