Recovering high quality surfaces from noisy point clouds, known as point
cloud denoising, is a fundamental yet challenging problem in geometry
processing. Most of the existing methods either directly denoise the noisy
input or filter raw normals followed by updating point positions. Motivated by
the essential interplay between point cloud denoising and normal filtering, we
revisit point cloud denoising from a multitask perspective, and propose an
end-to-end network, named PCDNF, to denoise point clouds via joint normal
filtering. In particular, we introduce an auxiliary normal filtering task to
help the overall network remove noise more effectively while preserving
geometric features more accurately. In addition to the overall architecture,
our network has two novel modules. On one hand, to improve noise removal
performance, we design a shape-aware selector to construct the latent tangent
space representation of the specific point by comprehensively considering the
learned point and normal features and geometry priors. On the other hand, point
features are more suitable for describing geometric details, and normal
features are more conducive for representing geometric structures (e.g., sharp
edges and corners). Combining point and normal features allows us to overcome
their weaknesses. Thus, we design a feature refinement module to fuse point and
normal features for better recovering geometric information. Extensive
evaluations, comparisons, and ablation studies demonstrate that the proposed
method outperforms state-of-the-arts for both point cloud denoising and normal
filtering