Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

•The surface is directly estimated from the sparse Structure-from-Motion data.•Both visibility and manifold constraints are enforced.•We experiment with hand-held and helmet-held low cost omnidirectional cameras.•Compact models of complete environments are obtained with low time complexity.

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-from-Motion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.