Hierarchical Surface Reconstruction from Multi-resolution Point Samples

Abstract

Robust surface reconstruction from sample points is a challenging problem, especially for real-world input data. We present a new hierarchical surface reconstruction based on volumetric graph-cuts that incorporates significant improvements over existing methods. One key aspect of our method is, that we exploit the footprint information which is inherent to each sample point and describes the underlying surface region represented by that sample. We interpret each sample as a vote for a region in space where the size of the region depends on the footprint size. In our method, sample points with large footprints do not destroy the fine detail captured by sample points with small footprints. The footprints also steer the inhomogeneous volumetric resolution used locally in order to capture fine detail even in large-scale scenes. Similar to other methods our algorithm initially creates a crust around the unknown surface. We propose a crust computation capable of handling data from objects that were only partially sampled, a common case for data generated by multi-view stereo algorithms. Finally, we show the effectiveness of our method on challenging outdoor data sets with samples spanning orders of magnitude in scale.