Region-Based Dense Depth Extraction from Multi-View Video
A novel multi-view region-based dense depth map estimation problem is presented, based on a modified plane-sweeping strategy. In this approach, the whole scene is assumed to be region-wise planar. These planar regions are defined by back-projections of the over-segmented homogenous color regions on the images and the plane parameters are determined by angle-sweeping at different depth levels. The position and rotation of the plane patches are estimated robustly by minimizing a segment-based cost function, which considers occlusions, as well. The quality of depth map estimates is measured via reconstruction quality of the conjugate views, after warping segments into these views by the resulting homographies. Finally, a greedy-search algorithm is applied to refine the reconstruction quality and update the plane equations with visibility constraint. Based on the simulation results, it is observed that the proposed algorithm handles large un-textured regions, depth discontinuities at object boundaries, slanted surfaces, as well as occlusions.