1 citations · 1 across the 4 of their papers we have counts for
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The NeRFect Match: Exploring NeRF Features for Visual Localization
Qunjie Zhou, Maxim Maximov, Or Litany +1
In this work, we propose the use of Neural Radiance Fields (NeRF) as a scene representation for visual localization. Recently, NeRF has been employed to enhance pose regression and…
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
Maxim Maximov, Tim Meinhardt, Ismail Elezi +4
The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition task…
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
Aysim Toker, Qunjie Zhou, Maxim Maximov +1
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. Th…
4D Panoptic LiDAR Segmentation
Mehmet Aygün, Aljoša Ošep, Mark Weber +4
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to ass…
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
Maxim Maximov, Kevin Galim, Laura Leal-Taixé
Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be par…
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
Maxim Maximov, Ismail Elezi, Laura Leal-Taixé
The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like peop…