Fast Spatially-Varying Indoor Lighting Estimation

Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Jean-Francois Lalonde; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6908-6917

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Garon_2019_CVPR,
author = {Garon, Mathieu and Sunkavalli, Kalyan and Hadap, Sunil and Carr, Nathan and Lalonde, Jean-Francois},
title = {Fast Spatially-Varying Indoor Lighting Estimation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}