Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00383813" target="_blank" >RIV/68407700:21230/25:00383813 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/25:00383813
Result on the web
<a href="https://doi.org/10.1109/CVPR52734.2025.00109" target="_blank" >https://doi.org/10.1109/CVPR52734.2025.00109</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52734.2025.00109" target="_blank" >10.1109/CVPR52734.2025.00109</a>

Result language
angličtina
Original language name
Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization
Original language description
Visual localization is the task of estimating a camera pose in a known environment. In this paper, we utilize 3D Gaussian Splatting (3DGS)-based representations for accurate and privacy-preserving visual localization. We propose Gaus sian Splatting Feature Fields (GSFFs), a scene represen tation for visual localization that combines an explicit ge ometry model (3DGS) with an implicit feature field. We leverage the dense geometric information and differentiable rasterization algorithm from 3DGS to learn robust feature representations grounded in 3D. In particular, we align a 3D scale-aware feature field and a 2D feature encoder in a common embedding space through a contrastive frame work. Using a 3D structure-informed clustering procedure, wefurther regularize the representation learning and seam lessly convert the features to segmentations, which can be used for privacy-preserving visual localization. Pose refine ment, which involves aligning either feature maps or seg mentations from a query image with those rendered from the GSFFsscenerepresentation, is used to achieve localization. Theresulting privacy- and non-privacy-preserving localiza tion pipelines, evaluated on multiple real-world datasets, show state-of-the-art performances.
Czech name
—
Czech description
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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)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3315-4364-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
1082-1092
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Nashville
Event date
Jun 11, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001562507801045

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