Unsupervised monocular depth estimation: Learning to generalize
Gonzales, Daniel
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https://hdl.handle.net/2142/108020
Description
Title
Unsupervised monocular depth estimation: Learning to generalize
Author(s)
Gonzales, Daniel
Issue Date
2020-05-11
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Monocular
Depth
Abstract
Models for unsupervised monocular depth estimation (MDE) have gained much attention due to recent breakthroughs and the ability to train with unlabeled data. Despite the state-of-the-art methods performing well on depth prediction benchmarks, certain artifacts and their performance compared to their supervised counterparts make them less favorable in certain domains. This thesis analyzes these models and presents a set of methods for improvement which can be applied in the training process.
Recent papers in unsupervised MDE focus on increasing performance metrics on the KITTI benchmark. We show that the results from these methods can be further improved by (i) providing synthetic training data via the game engine Grand Theft Auto V (GTAV) and (ii) applying data augmentation techniques that are consistent with the camera intrinsic parameters of the model.
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