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Monocular Depth Estimation with Edge-Based Constraints and Active Learning


Abstract The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These interpolated sparse depths are used to enforce additional constraints on the network’s predictions. In addition to the improved depth prediction performance obs... (more)
Created Date 2019
Contributor Rai, Anshul (Author) / Yang, Yezhou (Advisor) / Zhang, Wenlong (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Subject Computer science / Artificial intelligence / Artificial Intelligence / Computer Vision / Machine Learning / Monocular Depth Estimation / Robotics
Type Masters Thesis
Extent 75 pages
Language English
Copyright
Note Masters Thesis Computer Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis