Segmentation of sparse noisy point clouds using active contour models

This paper is concerned with the segmentation of noisy point clouds. The ability to partition a set of points into meaningful subsets is of broad interest, spanning such diverse fields as perceptual grouping and remote sensing. We present an approach that is based on projecting the point cloud onto a 2D image grid and applying active contour models (“snakes”) for partitioning point clouds efficiently and effectively. Although active contours were developed for use in image analysis, only a few researchers have considered their application to point-cloud segmentation. Previous systems do not perform well when a high level of noise is present. This paper discusses the heavy dependence of such systems on the initial placement of contours, and we present a novel approach to initializing these systems. Our results demonstrate that good performance is achievable with the approach of geometric active contours (GACs) when appropriately initialized.