Combining recognition and geometry for data-driven 3D reconstruction

Today's multi-view 3D reconstruction techniques rely almost exclusively on depth cues that come from multiple view geometry. While these cues can be used to produce highly accurate reconstructions, the resulting point clouds are often noisy and incomplete. Due to these issues, it may also be difficult to answer higher-level questions about the geometry, such as whether two surfaces meet at a right angle or whether a surface is planar. Furthermore, state-of-the-art reconstruction techniques generally cannot learn from training data, so having the ground-truth geometry for one scene does not aid in reconstructing similar scenes. In this work, we make two contributions toward data-driven 3D reconstruction. First, we present a dataset containing hundreds of RGBD videos that can be used as a source of training data for reconstruction algorithms. Second, we introduce the concept of the Shape Anchor, a region for which the combination of recognition and multiple view geometry allows us to accurately predict the latent, dense point cloud. We propose a technique to detect these regions and to predict their shapes, and we demonstrate it on our dataset.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Owens, Andrew (Andrew Hale)

Advisor dc:contributor.advisor

• William T. Freeman and Antonio Torralba.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/79237

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/79237