Abstract

Multi-View-Stereo (MVS) methods aim for the highest detail possible, however, such detail is often not required. In this work, we propose a novel surface reconstruction method based on image edges, superpixels and secondorder smoothness constraints, producing meshes comparable to classic MVS surfaces in quality but orders of magnitudes faster. Our method performs per-view dense depth optimization directly over sparse 3D Ground Control Points (GCPs), hence, removing the need for view pairing, image rectifification, and stereo depth estimation, and allowing for full per-image parallelization. We use Structure-fromMotion (SfM) points as GCPs, but the method is not specifific to these, e.g. LiDAR or RGB-D can also be used. The resulting meshes are compact and inherently edge-aligned with image gradients, enabling good-quality lightweight per-face flflat renderings. Our experiments demonstrate on a variety of 3D datasets the superiority in speed and competitive surface quality