NeRF for Outdoor Scene Relighting
This addresses the need for controllable lighting and viewpoint editing in outdoor scenes for applications like visual effects and virtual reality, representing a novel method for a known bottleneck.
The paper tackles the problem of photorealistic outdoor scene relighting from uncontrolled photos, presenting NeRF-OSR which enables simultaneous editing of illumination and viewpoint with higher quality and realistic self-shadowing compared to state-of-the-art methods.
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 environment map is captured together with a colour-calibration chequerboard to allow accurate numerical evaluations on real data against ground truth. Comparisons against SoTA show that NeRF-OSR enables controllable lighting and viewpoint editing at higher quality and with realistic self-shadowing reproduction. Our method and the dataset are publicly available at https://4dqv.mpi-inf.mpg.de/NeRF-OSR/.