Category-Agnostic Pose Estimation for Point Clouds
- URL: http://arxiv.org/abs/2403.07437v1
- Date: Tue, 12 Mar 2024 09:28:11 GMT
- Title: Category-Agnostic Pose Estimation for Point Clouds
- Authors: Bowen Liu, Wei Liu, Siang Chen, Pengwei Xie and Guijin Wang
- Abstract summary: The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input.
Both instance-based and category-based methods are unable to deal with unseen objects of unseen categories.
This paper proposes a method to introduce geometric features for pose estimation of point clouds without requiring category information.
- Score: 16.485852775220152
- License: http://creativecommons.org/licenses/by/4.0/
- 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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