Sampling and surface reconstruction of large scale point cloud

In this paper, we propose a new approach on sampling and surface reconstruction of large-scale point cloud data. The sampling method is for huge point cloud data using spatial curve order and the surface reconstruction approach being based on witness complex theory. The approach first reorders the point cloud according to the spatial curve order and then sequential samples the ordered data. The technique preserves the spatial characteristic of the point cloud data well, and it is also suitable for out-of-core implementation. After the sampling, we use witness complex theory to reconstruct a manifold triangle surface from sampling data under the constraint of original data. Experiments demonstrate that the proposed method improves the topological consistency of the reconstruction result.