Urban scene parsing via low-rank texture patches

Automatic 3-D reconstruction of city scenes from ground, aerial, and satellite imagery is a difficult problem that has seen active research for nearly two decades. The problem is difficult because many algorithms require salient areas in the image to be identified and segmented, a task that is typically done by humans. We propose a pipeline that detects these salient areas using low-rank texture patches. Areas in images such as building facades contain low-rank textures, which are an intrinsic property of the scene and invariant to viewpoint. The pipeline uses these low-rank patches to automatically rectify images and detect and segment out the patches with an energy-minimizing graph cut. The output is then further parameterized to provide useful data to existing 3-D reconstruction methods. The pipeline was evaluated on challenging test images from Microsoft Bing Maps oblique aerial photography and produced an 80% recall and precision with superb empirical results.

Department dc:contributor.department

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

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Lan, Cyril

Advisor dc:contributor.advisor

• Yi Ma and William T. Freeman.

• 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/1721.1/77536

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77536