Superpixel Meshes for Fast Edge Preserving Surface Reconstruction. скачать в хорошем качестве

Superpixel Meshes for Fast Edge Preserving Surface Reconstruction.

Superpixel Meshes for Fast Edge Preserving Surface Reconstruction Andras Bodis-Szomoru, Hayko Riemenschneider, Luc Van Gool CVPR 2015 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 second-order 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 rectification, and stereo depth estimation, and allowing for full per-image parallelization. We use Structure-from-Motion (SfM) points as GCPs, but the method is not specific 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 flat renderings. Our experiments demonstrate on a variety of 3D datasets the superiority in speed and competitive surface quality. This work was supported by the ERC Advanced Grant VarCity (#273940). https://varcity.ethz.ch/ Computer Vision Lab, ETH Zurich http://www.vision.ee.ethz.ch/