Iterative guidance normal filter for point cloud

Abstract

3D point clouds have become increasingly popular in recent year due to the rapid development of low-cost 3D sensors. One of the most interesting challenges is to filter point cloud, which undoubtedly becomes a crucial part of the point cloud processing pipeline. Based on normal information, this paper proposes a simple but effective point cloud filter framework. In this framework, a kd-tree structure is constructed for representing point cloud to search neighborhood and estimate normal for each point at first. Then, iteratively performing the processing that a bilateral filter is applied to the normal field obtained from the previous iteration, using the same normal field as the guidance; afterward, adjusting point positions is performed depending on the filtered normals. Experimental results indicate the effectiveness of our algorithms.