Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
Paper i 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.

Författare

Paul-Edouard Sarlin

Eidgenössische Technische Hochschule Zürich (ETH)

Ajaykumar Unagar

Eidgenössische Technische Hochschule Zürich (ETH)

Mans Larsson

Eigenvision AB

Hugo Germain

École des Ponts ParisTech

Carl Toft

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Viktor Larsson

Eidgenössische Technische Hochschule Zürich (ETH)

Marc Pollefeys

Microsoft Corporation

Eidgenössische Technische Hochschule Zürich (ETH)

Vincent Lepetit

École des Ponts ParisTech

Lars Hammarstrand

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Fredrik Kahl

Datorseende och medicinsk bildanalys

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021

1063-6919 (ISSN)

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


Online, ,

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Mediateknik

Datorseende och robotik (autonoma system)

DOI

10.1109/CVPR46437.2021.00326

Mer information

Senast uppdaterat

2022-02-10