Neural Radiance Fields for Outdoor Scene Relighting

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. It also includes a dedicated network for shadow reproduction, which is crucial for high-quality outdoor scene relighting. To evaluate the proposed method, we collect a new benchmark dataset of several outdoor sites, where each site is photographed from multiple viewpoints and at different timings. For each timing, a 360◦ environment map is captured together with a colour-calibration chequerboard to allow accurate numerical evaluations on real data against ground truth. Comparisons against state of the art show that NeRF-OSR enables controllable lighting and viewpoint editing at higher quality and with realistic self-shadowing reproduction. Our method and the dataset will be made publicly available at https://4dqv.mpi-inf.mpg. de/NeRF-OSR/.