1. 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

2. 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