TY - JOUR AU - Wang, Bing AU - Chen, Changhao AU - Xiaoxuan Lu, Chris AU - Zhao, Peijun AU - Trigoni, Niki AU - Markham, Andrew PY - 2020/04/03 Y2 - 2024/03/28 TI - AtLoc: Attention Guided Camera Localization JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 06 SE - AAAI Technical Track: Robotics DO - 10.1609/aaai.v34i06.6608 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6608 SP - 10393-10401 AB - <p>Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.</p> ER -