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SANet: Scene agnostic network for camera localization

Author: 
Date created: 
2021-03-04
Identifier: 
etd21281
Keywords: 
RGB camera localization
Scene agnostic
Scene coordinate regression
Deep learning
ICCV 2019
Abstract: 

This thesis presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other. Despite recent advancement in learning based methods with scene coordinate regression, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly. Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6 DoF camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance among methods working for arbitrary scenes without retraining or adaptation.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Ping Tan
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.
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