Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
Paper in proceeding, 2021

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc.

Author

Paul-Edouard Sarlin

Swiss Federal Institute of Technology in Zürich (ETH)

Ajaykumar Unagar

Swiss Federal Institute of Technology in Zürich (ETH)

Mans Larsson

Eigenvision AB

Hugo Germain

École des Ponts ParisTech

Carl Toft

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Viktor Larsson

Swiss Federal Institute of Technology in Zürich (ETH)

Marc Pollefeys

Microsoft Corporation

Swiss Federal Institute of Technology in Zürich (ETH)

Vincent Lepetit

École des Ponts ParisTech

Lars Hammarstrand

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Fredrik Kahl

Computer vision and medical image analysis

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

3246-3256
978-1-6654-4509-2 (ISBN)


Online, ,

Subject Categories

Bioinformatics (Computational Biology)

Media Engineering

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/CVPR46437.2021.00326

More information

Latest update

3/21/2023