We present an efficient method for geolocalization in urban environments starting from a coarse estimate of the location provided by a GPS and using a simple untextured 2.5D model of the surrounding buildings. Our key contribution is a novel efficient and robust method to optimize the pose: We train a Deep Network to predict the best direction to improve a pose estimate, given a semantic segmentation of the input image and a rendering of the buildings from this estimate. We then iteratively apply this CNN until converging to a good pose. This approach avoids the use of reference images of the surroundings, which are difficult to acquire and match, while 2.5D models are broadly available. We can therefore apply it to places unseen during training.
|Title of host publication||Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
|Publication status||Published - 2017|
|Event||2017 IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017 - Honolulu, United States|
Duration: 21 Jul 2017 → 26 Jul 2017
|Conference||2017 IEEE Conference on Computer Vision and Pattern Recognition|
|Abbreviated title||CVPR 2017|
|Period||21/07/17 → 26/07/17|