Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges. In this paper, we propose a novel deep learning method to jointly estimate normals and filter point clouds. We first introduce a 3D patch based contrastive learning framework, with noise corruption as an augmentation, to train a feature encoder capable of generating faithful representations of point cloud patches while remaining robust to noise. These representations are consumed by a simple regression network and supervised by a novel joint loss, simultaneously estimating point normals and displacements that are used to filter the patch centers. Experimental results show that our method well supports the two tasks simultaneously and preserves sharp features and fine details. It generally outperforms state-of-the-art techniques on both tasks. Our source code is available at https://github.com/ddsediri/CLJNEPCF.

翻译：点云滤波与法向估计是三维领域中的两个基础研究问题。现有方法通常分别进行法向估计与滤波，且往往对噪声敏感，或难以保留角点、边缘等尖锐几何特征。本文提出一种新型深度学习方法，可联合估计法向并过滤点云。我们首先引入基于三维面片的对比学习框架，以噪声扰动作为数据增强手段，训练出既能生成点云面片忠实表征、又对噪声保持鲁棒的特征编码器。该表征由简单回归网络处理，并受新型联合损失函数监督，可同时估计用于过滤面片中心的点法向与位移量。实验结果表明，本方法能同时良好支持两项任务，并保留尖锐特征与细微细节，在两项任务上普遍优于现有最优技术。源代码见 https://github.com/ddsediri/CLJNEPCF。