Improve 3D object reconstruction by using point and normal features together with PCDNF

Improve 3D object reconstruction by using point and normal features together with PCDNF

PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

arXiv paper abstract https://arxiv.org/abs/2209.00798v1

arXiv PDF paper https://arxiv.org/pdf/2209.00798v1.pdf

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.

... propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering ... network has two novel modules.

... to improve noise removal performance, ... 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.

... 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 ... method outperforms state-of-the-arts for both point cloud denoising and normal filtering.

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