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arXiv · 2403.07437

Category-Agnostic Pose Estimation for Point Clouds

Bowen Liu·Wei Liu·Siang Chen·Pengwei Xie·Guijin Wang
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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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Bowen Liu, Wei Liu, Siang Chen, Pengwei Xie, Guijin Wang. 2024-03-12. Category-Agnostic Pose Estimation for Point Clouds. https://arxiv.org/abs/2403.07437

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