  We propose a physically-motivated deep learning framework to solve a general
version of the challenging indoor lighting estimation problem. Given a single
LDR image with a depth map, our method predicts spatially consistent lighting
at any given image position. Particularly, when the input is an LDR video
sequence, our framework not only progressively refines the lighting prediction
as it sees more regions, but also preserves temporal consistency by keeping the
refinement smooth. Our framework reconstructs a spherical Gaussian lighting
volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially
consistent lighting prediction through volume ray tracing, a hybrid blending
network for detailed environment maps, an in-network Monte-Carlo rendering
layer to enhance photorealism for virtual object insertion, and recurrent
neural networks (RNN) to achieve temporally consistent lighting prediction with
a video sequence as the input. For training, we significantly enhance the
OpenRooms public dataset of photorealistic synthetic indoor scenes with around
360K HDR environment maps of much higher resolution and 38K video sequences,
rendered with GPU-based path tracing. Experiments show that our framework
achieves lighting prediction with higher quality compared to state-of-the-art
single-image or video-based methods, leading to photorealistic AR applications
such as object insertion.
