Algorithms for single-view depth image estimation

Depth sensing is fundamental in autonomous navigation, localization, and mapping. However, existing depth sensors offer many shortcomings, especially low effective spatial resolutions. In order to attain enhanced resolution with existing hardware, this dissertation studies the single-view depth estimation problem - the goal is to reconstruct the dense and complete 3D structures of the scene, given only sparse depth measurements. To this end, this thesis proposes three different algorithms for depth estimation. The first contribution is an algorithm for efficient reconstruction of 3D planar surfaces. This algorithm assumes that the 3D structure is piecewise-planar, and thus the second-order derivatives of the depth image are sparse. We develop a linear programming problem for recovery of the 3D surfaces under such assumptions, and provide conditions under which the reconstruction is exact.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Ma, Fangchang.

Advisor dc:contributor.advisor

• Sertac Karaman.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122371

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122371