Orienting unorganized points for surface reconstruction

We address the problem of assigning consistently oriented normal vectors to unorganized point cloud with noises, non-uniformities, and thinsharp features as a pre-processing step to surface reconstruction. The conventional orienting scheme using minimal spanning tree fails on points with the above defects. Different from the recently developed consolidation technique, our approach does not modify (i.e., down-sampling) the given point cloud so that we can reconstruct more surface details in the regions with very few points. The method consists of three major steps. We first propose a modified scheme of generating adaptive spherical cover for unorganized points by adding a sphere splitting step based on eigenvalue analysis. This modification can better preserve the connectivity of surface generated from the spheres in the highly sparse region. After generating the triangular mesh surface and cleaning its topology, a local search based algorithm is conducted to find the closest triangle to every input points and then specify their orientations. Lastly, an orientation-aware principle component analysis step gives correct and consistently oriented normal vectors to the unorganized input points. Conventional implicit surface fitting based approach can successfully reconstruct high quality surfaces from the unorganized point cloud with the help of consistently oriented normal vectors generated by our method. Keywords—orientation; consistency; unorganized points; PCA; surface reconstruction.