Robust Segmentation of Multiple Intersecting Manifolds from Unoriented Noisy Point Clouds

Abstract

We present a method for extracting complex manifolds with an arbitrary number of (self‐) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three‐step process. First, small flat neighbourhoods of all possible orientations are created around all points. Next, neighbourhoods are assembled into larger quasi‐flat patches, whose overlaps give the global connectivity structure of the point cloud. Finally, curved manifolds are extracted from the patch connectivity graph via a multiple‐source flood fill. The manifolds can be reconstructed into meshed surfaces using standard existing surface reconstruction methods. We demonstrate the speed and robustness of our method on several point clouds, with applications in point cloud segmentation, denoising and medial surface reconstruction.We present a method for extracting complex manifolds with an arbitrary number of (self) intersections from unoriented point clouds containing large amounts of noise. Manifolds are formed in a three step process. First, small flat neighborhoods of all possible orientations are created around all points. Next, neighborhoods are assembled into larger quasi‐flat patches, whose overlaps determine the global connectivity structure of the point cloud. Finally, curved manifolds, as well as their intersection curves, are extracted from the patch connectivity graph via a multiple‐source flood fill. The extracted manifolds can be straightforwardly reconstructed into polygonal surfaces using standard surface reconstruction methods.