  We present SOLID-Net, a neural network for spatially-varying outdoor lighting
estimation from a single outdoor image for any 2D pixel location. Previous work
has used a unified sky environment map to represent outdoor lighting. Instead,
we generate spatially-varying local lighting environment maps by combining
global sky environment map with warped image information according to geometric
information estimated from intrinsics. As no outdoor dataset with image and
local lighting ground truth is readily available, we introduce the SOLID-Img
dataset with physically-based rendered images and their corresponding intrinsic
and lighting information. We train a deep neural network to regress intrinsic
cues with physically-based constraints and use them to conduct global and local
lightings estimation. Experiments on both synthetic and real datasets show that
SOLID-Net significantly outperforms previous methods.
