@inproceedings{do2022learning,
author = {Do, Tien and Miksik, Ondrej and DeGol, Joseph and Park, Hyun Soo and Sinha, Sudipta},
title = {Learning to Detect Scene Landmarks for Camera Localization},
booktitle = {CVPR 2022},
year = {2022},
month = {May},
abstract = {Modern camera localization methods that use image retrieval, feature matching, and 3D structure-based pose estimation require long-term storage of numerous scene images or a vast amount of image features. This can make them unsuitable for resource constrained VR/AR devices and also raises serious privacy concerns. We present a new learned camera localization technique that eliminates the need to store features or a detailed 3D point cloud. Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible. We refer to these points as scene landmarks. We also show that a CNN can be trained to regress bearing vectors for such landmarks even when they are not within the camera’s field-of-view. We demonstrate that the predicted landmarks yield accurate pose estimates and that our method outperforms DSAC*, the state-of-theart in learned localization. Furthermore, extending HLoc (an accurate method) by combining its correspondences with our predictions boosts its accuracy even further.

https://youtu.be/HM2yLCLz5nY},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-to-detect-scene-landmarks-for-camera-localization/},
}