Fast Spatially-Varying Indoor Lighting Estimation

作者：Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean‐François Lalonde · 年份：2019 · DOI：10.1109/cvpr.2019.00707 · 被引用次数：136 · 研究领域：Video Surveillance and Tracking Methods、Advanced Vision and Imaging、Image Enhancement Techniques

We propose a real-time method to estimate spatially-varying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing approaches estimate a single, global lighting representation or require depth as input, our method reasons about local lighting without requiring any geometry information. We demonstrate, through quantitative experiments including a user study, that our results achieve lower lighting estimation errors and are preferred by users over the state-of-the-art. Our approach can be used directly for augmented reality applications, where a virtual object is relit realistically at any position in the scene in real-time.