Multi-Resolution Range Data Fusion for Multi-View Stereo Reconstruction

In this paper we present a probabilistic algorithm for multi-view reconstruction from calibrated images. The algorithm is based on multi-resolution volumetric range image integration and is highly separable as it only employs local optimization. Dense depth maps are transformed in an octree data structure with variable voxel sizes. This allows for an efficient modeling of point clouds with very variable density. A probability function constructed in discrete space is built locally with a Bayesian approach. Compared to other algorithms we can deal with extremely big scenes and complex camera configurations in a limited amount of time, as the solution can be split in arbitrarily many parts and computed in parallel. The algorithm has been applied to lab and outdoor benchmark data as well as to large image sets of urban regions taken by cameras on Unmanned Aerial Vehicles (UAVs) and from the ground, demonstrating high surface quality and good runtime performance.