Learning Scene Illumination by Pairwise Photos from Rear and Front Mobile Cameras
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Date
2018
Journal Title
Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Illumination estimation is an essential problem in computer vision, graphics and augmented reality. In this paper, we propose a learning based method to recover low-frequency scene illumination represented as spherical harmonic (SH) functions by pairwise photos from rear and front cameras on mobile devices. An end-to-end deep convolutional neural network (CNN) structure is designed to process images on symmetric views and predict SH coefficients. We introduce a novel Render Loss to improve the rendering quality of the predicted illumination. A high quality high dynamic range (HDR) panoramic image dataset was developed for training and evaluation. Experiments show that our model produces visually and quantitatively superior results compared to the state-of-the-arts. Moreover, our method is practical for mobile-based applications.
Description
@article{10.1111:cgf.13561,
journal = {Computer Graphics Forum},
title = {{Learning Scene Illumination by Pairwise Photos from Rear and Front Mobile Cameras}},
author = {Cheng, Dachuan and Shi, Jian and Chen, Yanyun and Deng, Xiaoming and Zhang, Xiaopeng},
year = {2018},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13561}
}