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title
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updated
2025-12-06 00:25:25
raw text
URF Urban Radiance Fields Konstantinos Rematas Andrew Liu Pratul P. Srinivasan Jonathan T. Barron Andrea Tagliasacchi Tom Funkhouser Vittorio Ferrari CVPR 2022 Paper Arxiv Video Abstract The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e.g., Street View). Given a sequence of posed RGB images and lidar sweeps acquired by cameras and scanners moving through an outdoor scene, we produce a model from which 3D surfaces can be extracted and novel RGB images can be synthesized. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings, with new methods for leveraging asynchronousl...
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