Self-Supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00381240" target="_blank" >RIV/68407700:21730/24:00381240 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/10550578" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10550578</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/3DV62453.2024.00139" target="_blank" >10.1109/3DV62453.2024.00139</a>

Result language

angličtina

Original language name

Self-Supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement

Original language description

Visual localization techniques rely upon some underlying scene representation to localize against. These representations can be explicit such as 3D SFM map or implicit, such as a neural network that learns to encode the scene. The former requires sparse feature extractors and matchers to build the scene representation. The latter might lack geometric grounding not capturing the 3D structure of the scene well enough. This paper proposes to jointly learn the scene representation along with a 3D dense feature field and a 2D feature extractor whose outputs are embedded in the same metric space. Through a contrastive framework we align this volumetric field with the image-based extractor and regularize the latter with a ranking loss from learned surface information. We learn the underlying geometry of the scene with an implicit field through volumetric rendering and design our feature field to leverage intermediate geometric information encoded in the implicit field. The resulting features are discriminative and robust to viewpoint change while maintaining rich encoded information. Visual localization is then achieved by aligning the image-based features and the rendered volumetric features. We show the effectiveness of our approach on real-world scenes, demonstrating that our approach outperforms prior and concurrent work on leveraging implicit scene representations for localization.

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

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

3DV2024: Proceedings of the 2024 International Conference in 3D Vision

ISBN

979-8-3503-6246-6

ISSN

2378-3826

e-ISSN

2475-7888

Number of pages

11

Pages from-to

484-494

Publisher name

IEEE Computer Society

Place of publication

Los Alamitos

Event location

Davos

Event date

Mar 18, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001250581700038