@inproceedings{schnberger2018semantic,
author = {Schönberger, Johannes L. and Pollefeys, Marc and Geiger, Andreas and Sattler, Torsten},
title = {Semantic Visual Localization},
organization = {IEEE/CVF},
booktitle = {2018 Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018},
month = {April},
abstract = {Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it to succeed under conditions where previous approaches failed. Our method leverages a novel generative model for descriptor learning, trained on semantic scene completion as an auxiliary task. The resulting 3D descriptors are robust to missing observations by encoding high-level 3D geometric and semantic information. Experiments on several challenging large-scale localization datasets demonstrate reliable localization under extreme viewpoint, illumination, and geometry changes.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/semantic-visual-localization/},
}