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.

翻译：从含噪点云中恢复高质量表面（称为点云去噪）是几何处理中一个基础但具有挑战性的问题。现有方法大多直接对含噪输入进行去噪，或先滤波原始法向量再更新点位置。受点云去噪与法向滤波之间本质交互关系的启发，我们从多任务视角重新审视点云去噪问题，提出一个名为PCDNF的端到端网络，通过联合法向滤波实现点云去噪。具体而言，我们引入一个辅助的法向滤波任务，帮助整体网络在更精确保留几何特征的同时更有效地去除噪声。除整体架构外，该网络包含两个新型模块：一方面，为提升去噪性能，我们设计了一个形状感知选择器，通过综合学习到的点特征、法向特征及几何先验，构建特定点的潜在切空间表示；另一方面，点特征更适用于描述几何细节，而法向特征更利于表征几何结构（如尖锐边缘和角点）。融合点特征与法向特征能够克服二者各自的局限性。为此，我们设计了一个特征精炼模块，将点特征与法向特征进行融合以更好地恢复几何信息。广泛的评估、对比及消融实验表明，所提方法在点云去噪和法向滤波任务上均优于现有最先进技术。