NeRF for Outdoor Scene Relighting
Viktor Rudnev, Mohamed Elgharib, William Smith, Lingjie Liu, and Vladislav Golyanik, Christian Theobalt

TL;DR
NeRF-OSR introduces a novel neural radiance field approach for outdoor scene relighting, enabling high-quality, controllable lighting and viewpoint editing from uncontrolled photographs, supported by a new benchmark dataset.
Contribution
It is the first method to achieve outdoor scene relighting with neural radiance fields using only uncontrolled photos, allowing direct illumination control and high-quality editing.
Findings
Outperforms state-of-the-art in relighting quality
Enables realistic self-shadowing effects
Supports simultaneous lighting and viewpoint editing
Abstract
Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reflectance and illumination. A delicate manipulation of the lighting can then be performed while keeping the scene albedo and geometry unaltered. We present NeRF-OSR, i.e., the first approach for outdoor scene relighting based on neural radiance fields. In contrast to the prior art, our technique allows simultaneous editing of both scene illumination and camera viewpoint using only a collection of outdoor photos shot in uncontrolled settings. Moreover, it enables direct control over the scene illumination, as defined through a spherical harmonics model. For evaluation, we collect a new benchmark dataset of several outdoor sites photographed from multiple viewpoints and at different times. For each time, a 360 degree…
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Taxonomy
TopicsAdvanced Vision and Imaging · Computer Graphics and Visualization Techniques · Remote Sensing and LiDAR Applications
