Non-Local Part-Aware Point Cloud Denoising

• URL: http://arxiv.org/abs/2003.06631v1
• Date: Sat, 14 Mar 2020 13:51:50 GMT
• Title: Non-Local Part-Aware Point Cloud Denoising
• Authors: Chao Huang, Ruihui Li, Xianzhi Li, and Chi-Wing Fu
• Abstract summary: This paper presents a novel non-local part-aware deep neural network to denoise point clouds. We design the non-local learning unit (NLU) customized with a graph attention module to adaptively capture non-local semantically-related features. To enhance the denoising performance, we cascade a series of NLUs to progressively distill the noise features from the noisy inputs.
• Score: 55.50360085086123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes. Different from existing works that explore small local patches, we design the non-local learning unit (NLU) customized with a graph attention module to adaptively capture non-local semantically-related features over the entire point cloud. To enhance the denoising performance, we cascade a series of NLUs to progressively distill the noise features from the noisy inputs. Further, besides the conventional surface reconstruction loss, we formulate a semantic part loss to regularize the predictions towards the relevant parts and enable denoising in a part-aware manner. Lastly, we performed extensive experiments to evaluate our method, both quantitatively and qualitatively, and demonstrate its superiority over the state-of-the-arts on both synthetic and real-scanned noisy inputs.

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