Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/124684
Type: Conference paper
Title: A hybrid probabilistic model for camera relocalization
Author: Cai, M.
Shen, C.
Reid, I.
Citation: Proceedings of the 29th British Machine Vision Conference (BMVC 2018), 2019 / pp.1-12
Publisher: BMVC Press
Issue Date: 2019
Conference Name: British Machine Vision Conference (BMVC) (03 Sep 2018 - 06 Sep 2018 : Newcastle upon Tyne, UK)
Statement of
Responsibility: 
Ming Cai, Chunhua Shen, Ian Reid
Abstract: We present a hybrid deep learning method for modelling the uncertainty of camera relocalization from a single RGB image. The proposed system leverages the discriminative deep image representation from a convolutional neural networks, and uses Gaussian Process regressors to generate the probability distribution of the six degree of freedom (6DoF) camera pose in an end-to-end fashion. This results in a network that can generate uncertainties over its inferences with no need to sample many times. Furthermore we show that our objective based on KL divergence reduces the dependence on the choice of hyperparameters. The results show that compared to the state-of-the-art Bayesian camera relocalization method, our model produces comparable localization uncertainty and improves the system efficiency significantly, without loss of accuracy.
Rights: © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
RMID: 1000000489
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FT120100969
http://purl.org/au-research/grants/arc/FL130100102
Published version: http://bmvc2018.org/programmedetail.html
Appears in Collections:Computer Science publications

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