Non-Local Part-Aware Point Cloud Denoising
This addresses noise reduction in 3D point clouds for applications like computer vision and robotics, representing an incremental improvement with novel components.
The paper tackles point cloud denoising by proposing a non-local part-aware deep neural network that leverages non-local self-similarity and semantic parts, achieving superior performance over state-of-the-art methods on synthetic and real-scanned data.
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.