Point cloud denoising with adaptive graph convolution

Yi Liu; Yuan Wang; Jiapeng Li

doi:10.1117/12.2671483

29 March 2023 Point cloud denoising with adaptive graph convolution

Yi Liu, Yuan Wang, Jiapeng Li

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 125942B (2023) https://doi.org/10.1117/12.2671483
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

Point cloud denoising is a key aspect of current point cloud data processing. Existing deep learning-based denoising methods rely on local feature information obtained from noisy point clouds as the basis for point displacement. However, in the presence of noise, the local features of points usually cannot accurately represent the underlying surface of the point cloud. We propose a new deep learning-based denoising method using the graph convolution operator, which can compute feature aggregation weights adaptively based on feature vectors to achieve hierarchical distinction between noise and the underlying surface. Meanwhile, in order to alleviate the phenomenon that the predicted gradient fluctuates more drastically during the iterative process, we introduce the momentum rise method in the gradient iteration process to improve the stability of the solution. We evaluated our approach on both toy datasets and real world datasets. Under different types and levels of noise disturbances, our method has better results compared to other models.

Citation Download Citation

Yi Liu, Yuan Wang, and Jiapeng Li "Point cloud denoising with adaptive graph convolution", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 125942B (29 March 2023); https://doi.org/10.1117/12.2671483

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