A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images. However, in most cases imagesequences, rather only single images, are readily available. To this extent, none of the proposed learning-based approaches exploit the valuable constraint of temporal smoothness, often leading to situations where the per-frame error is larger than the camera motion. In this paper we
doi:10.1109/cvpr.2017.284
dblp:conf/cvpr/ClarkWMTW17
fatcat:ltmhuc3vrvg7rbialdlmb3uace