Poster Spotlights Shape Anchors for Data-driven Session 1A: Tuesday Morning, December 3rd Shape Anchors for Data-driven Multi-view Reconstruction Andrew Owens Jianxiong Xiao Antonio Torralba William Freeman

Shape Anchors for Data-driven Multi-view Reconstruction P1A-01 Align to sparse points Matches from RGB-D database Transfer good matches The goal of our work is to build dense 3D reconstructions from videos. Our approach is based on the idea of "shape anchoring". There are some image patches, such as the one highlighted here, that are highly informative about the geometry of the scene. We call these patches shape anchors, and we estimate their dense 3D geometry by transferring depth from examples in a database. We then use multi-view cues to resolve ambiguities and remove inaccurate matches. We align the matched 3D shape to a sparse stereo point cloud, and we transfer the geometry of good matches Starting with a sparse multi-view stereo point cloud, we use shape anchors and the high-confidence transferred geometry to perform multi-view reconstruction tasks, resulting in reconstruction that is denser than the one provided by multi-view stereo alone. Shape anchor