HDR Illumination Outpainting with a Two-Stage GAN Model

In this paper we present a method for single-view illumination estimation of indoor scenes, using image-based lighting, that incorporates state-of-the-art outpainting methods. Recent advancements in illumination estimation have focused on improving the detail of the generated environment map so it can realistically light mirror reflective surfaces. These generated maps often include artefacts at the borders of the image where the panorama wraps around. In this work we make the key observation that inferring the panoramic HDR illumination of a scene from a limited field of view LDR input can be framed as an outpainting problem (whereby the original image must be expanded beyond its original borders). We incorporate two key techniques used in outpainting tasks: i) separating the generation into multiple networks (a diffuse lighting network and a high-frequency detail network) to reduce the amount to be learnt by a single network, ii) utilising an inside-out method of processing the input image to reduce the border artefacts. Further to incorporating these outpainting methods we also introduce circular padding before the network to help remove the border artefacts. Results show the proposed approach is able to relight diffuse, specular and mirror surfaces more accurately than existing methods in terms of the position of the light sources and pixelwise accuracy, whilst also reducing the artefacts produced at the borders of the panorama.