Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior-Supplementary Material-James A. D. Gardner

Here we provide further results, additional training information, and explore the how RENI performs when fit to illumination maps that contain unnatural illumination conditions. A.1 Cosine Loss When optimising just the latent codes to unseen environment maps, we found improved performance when including a cosine similarity loss. This loss is used again during the inverse rendering task. However as the images used in that loss are not equirectangular the sin(θ(d)) term is not included. A.2 Gamma Correction For display, all linear HDR images I had their gamma adjusted using the following process: 1. Adjust exposure to set the white level to the p-th percentile (p = 98) I I percentile(I, p) 2. Clamp between [0, 1] I clamp(I, 0, 1) 3. Apply gamma correction using the standard sRGB gamma curve: { We include additional qualitative results of the RENI model.