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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00356128" target="_blank" >RIV/68407700:21730/21:00356128 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CVPR46437.2021.00326" target="_blank" >https://doi.org/10.1109/CVPR46437.2021.00326</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR46437.2021.00326" target="_blank" >10.1109/CVPR46437.2021.00326</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

ISBN

978-1-6654-4510-8

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

11

Pages from-to

3246-3256

Publisher name

IEEE Computer Society

Place of publication

USA

Event location

Nashville

Event date

Jun 20, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000739917303044