Maximally informative surface reconstruction from lines

In this paper, we propose a novel multi-view method for surface reconstruction from matched line segments with applications to robotic mapping and image-based rendering. Starting from 3D line segments, plane hypotheses are formed for all non-collinear and sufficiently coplanar segment pairs. The surface that is spanned by two segments is used to immediately prune hypotheses that do not pass a sight line occlusion check to keep the initial plane number tractable. After further merging, exhaustive intersections are computed in an efficient way to yield a maximally informative surface representation. Finally, robustified visibility constraints are used to recover a dense surface mesh that is a pessimistic representation of the free space, which is desirable for path planning applications. The presented system is a complete and automatic solution suitable for mapping an environment in realtime scenarios like robotic exploration. We demonstrate the performance of our algorithm on several indoor scenes with varying complexity.