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Adaptive Losses for Camera Pose Supervision

This master thesis studies the learning of dense feature descriptors where camera poses are the only supervisory signal. The use of camera poses as a supervisory signal has only been published once before, and this thesis expands on this previous work by utilizing a couple of different techniques meant increase the robustness of the method, which is particularly important when not having access to ground-truth correspondences. Firstly, an adaptive robust loss is utilized to better differentiate inliers and outliers. Secondly, statistical properties during training are both enforced and adapted to, in an attempt to alleviate problems with uncertainties introduced by not having true correspondences available. These additions are shown to slightly increase performance, and also highlights some key ideas related to prediction certainty and robustness when working with camera poses as a supervisory signal. Finally, possible directions for future work are discussed.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-177339
Date January 2021
CreatorsDahlqvist, Marcus
PublisherLinköpings universitet, Datorseende
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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