Unsupervised monocular depth estimation: Learning to generalize

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Gonzales, Daniel

Contributors dc:contributor

• Do, Minh

• Monocular
• Depth

Statement dc:rights

• Copyright 2020 Daniel Gonzales

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108020

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108020