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dc.contributor.authorXu, Zilinen_US
dc.contributor.authorSun, Qiangen_US
dc.contributor.authorWang, Luen_US
dc.contributor.authorXu, Yanningen_US
dc.contributor.authorWang, Beibeien_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.description.abstractGradient-domain rendering can highly improve the convergence of light transport simulation using the smoothness in image space. These methods generate image gradients and solve an image reconstruction problem with rendered image and the gradient images. Recently, a previous work proposed a gradient-domain volumetric photon density estimation for homogeneous participating media. However, the image reconstruction relies on traditional L1 reconstruction, which leads to obvious artifacts when only a few rendering passes are performed. Deep learning based reconstruction methods have been exploited for surface rendering, but they are not suitable for volume density estimation. In this paper, we propose an unsupervised neural network for image reconstruction of gradient-domain volumetric photon density estimation, more specifically for volumetric photon mapping, using a variant of GradNet with an encoded shift connection and a separated auxiliary feature branch, which includes volume based auxiliary features such as transmittance and photon density. Our network smooths the images on global scale and preserves the high frequency details on a small scale. We demonstrate that our network produces a higher quality result, compared to previous work. Although we only considered volumetric photon mapping, it's straightforward to extend our method for other forms, like beam radiance estimation.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectNeural network
dc.subjectRay tracing
dc.titleUnsupervised Image Reconstruction for Gradient-Domain Volumetric Renderingen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersLights and Ray Tracing

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  • 39-Issue 7
    Pacific Graphics 2020 - Symposium Proceedings

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