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Omni-nerf: neural radiance field from 360° image captures


This paper tackles the problem of novel view synthesis (NVS) from 360° images with imperfect camera poses or intrinsic parameters. We propose a novel end-to-end framework for training Neural Radiance Field (NeRF) models given only 360° RGB images and their rough poses, which we refer to as Omni-NeRF. We extend the pinhole camera model of NeRF to a more general camera model that better fits omni-directional fish-eye lenses. The approach jointly learns the scene geometry and optimizes the camera parameters without knowing the fisheye projection.
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hal-03646688 , version 1 (19-04-2022)


  • HAL Id : hal-03646688 , version 1


Kai Gu, Thomas Maugey, Sebastian Knorr, Christine Guillemot. Omni-nerf: neural radiance field from 360° image captures. ICME 2022 - IEEE international conference on multimedia and expo, Jul 2022, Taipei, Taiwan. pp.1-6. ⟨hal-03646688⟩
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