Please use this identifier to cite or link to this item:
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) (3 Sep 2018 - 6 Sep 2018 : Newcastle upon Tyne, UK)
Statement of
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.
Grant ID:
Published version:
Appears in Collections:Aurora harvest 4
Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_124684.pdfPublished version3.43 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.