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Editable Indoor Lighting Estimation

Henrique Weber, Mathieu Garon, Jean-François Lalonde

Submitted on 7 November 2022, last revised on 9 November 2022

Abstract

We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estimates a parametric light that is easy to edit and allows renderings with strong shadows, alongside with a non-parametric texture with high-frequency information necessary for realistic rendering of specular objects. Once estimated, the predictions obtained with our model are interpretable and can easily be modified by an artist/user with a few mouse clicks. Quantitative and qualitative results show that our approach makes indoor lighting estimation easier to handle by a casual user, while still producing competitive results.

Preprint

Comment: ECCV 2022

Subject: Computer Science - Computer Vision and Pattern Recognition

URL: http://arxiv.org/abs/2211.03928