Journal article

A Recurrent Deep Network for Estimating the Pose of Real Indoor Images from Synthetic Image Sequences

Debaditya Acharya, Sesa Singha Roy, Kourosh Khoshelham, Stephan Winter

SENSORS | MDPI | Published : 2020

Abstract

Recently, deep convolutional neural networks (CNN) have become popular for indoor visual localisation, where the networks learn to regress the camera pose from images directly. However, these approaches perform a 3D image-based reconstruction of the indoor spaces beforehand to determine camera poses, which is a challenge for large indoor spaces. Synthetic images derived from 3D indoor models have been used to eliminate the requirement of 3D reconstruction. A limitation of the approach is the low accuracy that occurs as a result of estimating the pose of each image frame independently. In this article, a visual localisation approach is proposed that exploits the spatio-temporal information fr..

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Grants

Awarded by LIEF Grant


Funding Acknowledgements

This research is supported by a Research Engagement Grant from the Melbourne School of Engineering and a Melbourne Research Scholarship. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200.