A single-stage point cloud cleaning network for outlier removal and denoising

As a simple, flexible and effective representation for objects, 3D point cloud has attracted more and more attention in recent years. However, raw point clouds obtained from 3D scanners or image-based reconstruction techniques are often contaminated with noise and outliers, which hinders downstream tasks such as object classification, surface reconstruction, and so on. Therefore, point cloud cleaning, i.e., removing noisy points and outliers from raw point cloud, is a prior step of most geometry processing workflows. The exiting techniques for point cloud cleaning usually include two stages, that is, discarding outliers at first, and then denoising the resulting point cloud. This two-stage process usually requires two different models, which is cumbersome to train and use. To solve this problem, a novel data driven method, named SSPCN (single-stage point cloud cleaning network), is proposed in this paper. SSPCN can simultaneously remove outliers and denoise a point cloud in a single model. Specifically, SSPCN is consisted of adaptive downsampling module, feature compensation module, upsampling module and coordinate reconstruction module. Given a raw point cloud as input, the downsampling module is first used to obtain a prefiltered point cloud subset and learn initial features of the subset. The feature compensation module is then utilized to learn accurate features from initial features. Next, the upsampling module upsamples the features to restore the original size of the point cloud. Last, the coordinate reconstruction module generates a cleaned point cloud from upsampled features. SSPCN is validated both on synthetic and real scanned data. Extensive experiments demonstrate that SSPCN outperforms state-of-the-art point cloud cleaning techniques in terms of quantitative metric and visual quality.