RePCD-Net: Feature-Aware Recurrent Point Cloud Denoising Network

The captured 3D point clouds by depth cameras and 3D scanners are often corrupted by noise, so point cloud denoising is typically required for downstream applications. We observe that: (i) the scale of the local neighborhood has a significant effect on the denoising performance against different noise levels, point intensities, as well as various kinds of local details; (ii) non-iteratively evolving a noisy input to its noise-free version is non-trivial; (iii) both traditional geometric methods and learning-based methods often lose geometric features with denoising iterations, and (iv) most objects can be regarded as piece-wise smooth surfaces with a small number of features. Motivated by these observations, we propose a novel and task-specific point cloud denoising network, named RePCD-Net, which consists of four key modules: (i) a recurrent network architecture to effectively remove noise; (ii) an RNN-based multi-scale feature aggregation module to extract adaptive features in different denoising stage; (iii) a recurrent propagation layer to enhance the geometric feature perception across stages; and (iv) a feature-aware CD loss to regularize the predictions towards multi-scale geometric details. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and superiority of our method over state-of-the-arts, in terms of noise removal and feature preservation.