Spatially-Varying Outdoor Lighting Estimation from Intrinsics
This work addresses outdoor lighting estimation for computer vision applications, but it is incremental as it builds on existing methods with a novel dataset and approach.
The paper tackles the problem of estimating spatially-varying outdoor lighting from a single image by introducing SOLID-Net, which generates local lighting environment maps using geometric information from intrinsics, and it significantly outperforms previous methods on synthetic and real datasets.
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.