Point cloud denoising has been an attractive problem in geometry processing. The main challenge is to eliminate noise while preserving different levels of features and preventing unnatural effects (such as over-sharpened artifacts on smoothly curved faces and cross artifacts on sharp edges). In this paper, we propose a novel feature-preserving framework to achieve these goals. Firstly, we newly define some discrete operators on point clouds, which can be used to construct a second order regularization for restoring a point normal field. Then, based on the filtered normals, we perform a feature detection step by a bi-tensor voting scheme. As will be seen, it is robust against noise and can locate underlying geometric features accurately. Finally, we reposition points with a multi-normal strategy by using a simple yet effective RANSAC-based algorithm. Intensive experimental results show that the proposed method performs favorably compared to other state-of-the-art approaches.

在几何处理中，点云去噪一直是一个有吸引力的问题。主要挑战是要消除噪声，同时保留不同级别的特征并防止产生不自然的效果（例如，平滑曲面上的过度锐化的伪像和尖锐边缘上的交叉伪像）。在本文中，我们提出了一个新颖的功能保留框架来实现这些目标。首先，我们在点云上新定义了一些离散算子，这些算子可用于构造二阶正则化以恢复点法向场。然后，基于过滤后的法线，我们通过双张量投票方案执行特征检测步骤。可以看出，它具有强大的抗噪能力，可以准确地定位潜在的几何特征。最后，我们通过使用一个简单而有效的基于RANSAC的算法，使用多范数策略重新定位点。密集的实验结果表明，与其他最新方法相比，该方法的性能更好。