Edirimuni Dasith de Silva, Lu Xuequan, Li Gang, Robles-Kelly Antonio
IEEE Trans Vis Comput Graph. 2024 Aug;30(8):4527-4541. doi: 10.1109/TVCG.2023.3263866. Epub 2024 Jul 1.
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 article, 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.
点云滤波和法线估计是三维领域中的两个基本研究问题。现有方法通常分别进行法线估计和滤波，并且常常对噪声敏感和/或无法保留诸如角点和边缘等尖锐几何特征。在本文中，我们提出了一种新颖的深度学习方法来联合估计法线和滤波点云。我们首先引入一个基于三维面片的对比学习框架，以噪声破坏作为一种增强方式，来训练一个特征编码器，该编码器能够生成点云面片的忠实表示，同时对噪声保持鲁棒性。这些表示由一个简单的回归网络处理，并由一种新颖的联合损失进行监督，同时估计用于滤波面片中心的点法线和位移。实验结果表明，我们的方法能够很好地同时支持这两个任务，并保留尖锐特征和精细细节。在这两个任务上，它总体上优于现有技术。
