@inproceedings{
he2024domain,
title={Domain Generalization-Aware Uncertainty Introspective Learning for 3D Point Clouds Segmentation},
author={Pei He and Licheng Jiao and Lingling Li and Xu Liu and Fang Liu and Wenping Ma and Shuyuan Yang and Ronghua Shang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=zoMT1Czqv3}
}
Domain generalization 3D segmentation aims to learn the point clouds with unknown distributions. Feature augmentation has been proven to be effective for domain generalization. However, each point of the 3D segmentation scene contains uncertainty in the target domain, which affects model generalization. This paper proposes the Domain Generalization-Aware Uncertainty Introspective Learning (DGUIL) method, including Potential Uncertainty Modeling (PUM) and Momentum Introspective Learning (MIL), to deal with the point uncertainty in domain shift. Specifically, PUM explores the underlying uncertain point cloud features and generates the different distributions for each point. The PUM enhances the point features over an adaptive range, which provides various information for simulating the distribution of the target domain. Then, MIL is designed to learn generalized feature representation in uncertain distributions. The MIL utilizes uncertainty correlation representation to measure the predicted divergence of knowledge accumulation, which learns to carefully judge and understand divergence through uncertainty introspection loss. Finally, extensive experiments verify the advantages of the proposed method over current state-of-the-art methods. The code will be available.
Domain Generalization-Aware Uncertainty Introspective Learning for 3D Point Clouds Segmentation
Pei He Licheng Jiao Lingling Li Xu Liu Fang Liu Wenping Ma Shuyuan Yang Ronghua Shang