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|Title:||GMLight: lighting estimation via geometric distribution approximation||Authors:||Zhan, Fangneng
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2022||Source:||Zhan, F., Yu, Y., Zhang, C., Wu, R., Hu, W., Lu, S., Ma, F., Xie, X. & Shao, L. (2022). GMLight: lighting estimation via geometric distribution approximation. IEEE Transactions On Image Processing, 31, 2268-2278. https://dx.doi.org/10.1109/TIP.2022.3151997||Journal:||IEEE Transactions on Image Processing||Abstract:||Inferring the scene illumination from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization. This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, which can be estimated by a regression network. Inspired by the earth mover's distance, we design a novel geometric mover's loss to guide the accurate regression of light distribution parameters. With the estimated light parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and high-frequency details. Extensive experiments show that GMLight achieves accurate illumination estimation and superior fidelity in relighting for 3D object insertion. The codes are available at https://github.com/fnzhan/Illumination-Estimation.||URI:||https://hdl.handle.net/10356/157035||ISSN:||1057-7149||DOI:||10.1109/TIP.2022.3151997||Rights:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2022.3151997.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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