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Denoising point clouds with fewer learnable parameters
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AI
DOI：10.1016/j.cad.2024.103708.png)
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Abstract
En 中文
Point cloud denoising is a crucial task in the field of geometric processing. Recent years have witnessed significant advancements in deep learning-based point cloud denoising algorithms. These methods, compared to traditional techniques, demonstrate enhanced robustness against noise and produce point cloud data of higher fidelity. Despite their impressive performance, achieving a balance between denoising efficacy and computational efficiency remains a formidable challenge in learning-based methods. To solve this problem, we introduce LPCDNet, a novel lightweight point cloud denoising network. LPCDNet consists of three main components: a lightweight feature extraction module utilizing trigonometric functions for relative position encoding; a nonparametric feature aggregation module to leverage semantic similarities for global context comprehension; and a decoder module designed to realign noise points with the underlying surface. The network is designed to capture both local details and non-local structures, thereby ensuring high-quality denoising outcomes with a minimal parameter count. Extensive experimental evaluations demonstrate that LPCDNet achieves comparable or superior performance to state-of-the-art methods, while significantly reducing the number of learnable parameters and the necessary running time.
Keywords:
Point cloud
Point cloud denoising
Non-local feature