@inproceedings{
chen2024progressive,
title={Progressive Point Cloud Denoising with Cross-Stage Cross-Coder Adaptive Edge Graph Convolution Network},
author={Wu Chen and Hehe Fan and Qiuping Jiang and Chao Huang and Yi Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=LOJdqiqbAt}
}
Due to the limitation of collection device and unstable scanning process, point cloud data is usually noisy. Those noise deforms the underlying structures of point clouds and inevitably affects downstream tasks such as rendering, reconstruction and analysis. In this paper, we propose a Cross-stage Cross-coder Adaptive Edge Graph Convolution Network (C$^{2}$AENet) to denoise point clouds. Our network uses multiple stages to progressively and iteratively denoise points. To improve the effectiveness, we add connections between two stages and between the encoder and decoder, leading to the cross-stage cross-coder architecture. Additionally, existing graph-based point cloud learning methods tend to capture local structure. They typically construct a semantic graph based on semantic distance, which may ignore Euclidean neighbors and lead to insufficient geometry perception. Therefore, we introduce a geometric graph and adaptively calculate edge attention based on the local and global structural information of the points. This results in a novel graph convolution module that allows the network to capture richer contextual information and focus on more important parts. Extensive experiments demonstrate that the proposed method is competitive compared with other state-of-the-art methods. The code will be made publicly available.
Progressive Point Cloud Denoising with Cross-Stage Cross-Coder Adaptive Edge Graph Convolution Network
Wu Chen Hehe Fan Qiuping Jiang Chao Huang Yi Yang