Automatic Spatially Varying Illumination Recovery of Indoor Scenes Based on a Single RGB-D Image

Abstract : We propose an automatic framework to recover the illumination of indoor scenes based on a single RGB-D image. Unlike previous works, our method can recover spatially varying illumination without using any lighting capturing devices or HDR information. The recovered illumination can produce realistic rendering results. To model the geometry of the visible and invisible parts of scenes corresponding to the input RGB-D image, we assume that all objects shown in the image are located in a box with six faces and build a geometry model based on the depth map. We then present a confidence-scoring based strategy to separate the light sources from the highlight areas. The positions of light sources both in and out of the camera's view are calculated based on the classification result and the recovered geometry model. Finally, an iterative procedure is proposed to calculate the colors of light sources and the materials in the scene. In addition, a data-driven method is used to set constraints on the light source intensities. Using the estimated light sources and geometry model, environment maps at different points in the scene are generated that can model the spatial variance of illumination. The experimental results demonstrate the validity of our approach.
Type de document :
Article dans une revue
IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2018, pp.1-14. 〈10.1109/TVCG.2018.2876541〉
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https://hal.archives-ouvertes.fr/hal-01907554
Contributeur : Xavier Granier <>
Soumis le : lundi 29 octobre 2018 - 11:31:15
Dernière modification le : lundi 12 novembre 2018 - 13:40:37

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Guanyu Xing, Yanli Liu, Haibin Ling, Xavier Granier, Yanci Zhang. Automatic Spatially Varying Illumination Recovery of Indoor Scenes Based on a Single RGB-D Image. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2018, pp.1-14. 〈10.1109/TVCG.2018.2876541〉. 〈hal-01907554〉

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