论文标题
关节正常估计和点云过滤的对比度学习
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
论文作者
论文摘要
点云过滤和正常估计是3D场中的两个基本研究问题。现有方法通常会单独执行正常的估计和过滤，并且通常对噪声和/或无法保留尖锐的几何特征（例如角和边缘）表示敏感性。在本文中，我们提出了一种新颖的深度学习方法，以共同估计正态和过滤点云。我们首先引入了一个基于3D补丁的对比学习框架，并以噪声损坏为增强，以训练能够产生点云贴片的忠实表示的功能编码器，同时保持噪音的强大功能。这些表示由简单的回归网络消耗，并通过新的关节损失进行监督，同时估算用于过滤贴片中心的点正常和位移。实验结果表明，我们的方法很好地支持了这两个任务，并保留了尖锐的功能和细节。通常，它在这两个任务上都胜过最先进的技术。我们的源代码可在https://github.com/ddsediri/cljnepcf上找到。
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.
