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Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing. (arXiv:2211.03017v1 [cs.CV])
Nov. 8, 2022, 2:15 a.m. | Jingsen Zhu, Fujun Luan, Yuchi Huo, Zihao Lin, Zhihua Zhong, Dianbing Xi, Jiaxiang Zheng, Rui Tang, Hujun Bao, Rui Wang
cs.CV updates on arXiv.org arxiv.org
Indoor scenes typically exhibit complex, spatially-varying appearance from
global illumination, making inverse rendering a challenging ill-posed problem.
This work presents an end-to-end, learning-based inverse rendering framework
incorporating differentiable Monte Carlo raytracing with importance sampling.
The framework takes a single image as input to jointly recover the underlying
geometry, spatially-varying lighting, and photorealistic materials.
Specifically, we introduce a physically-based differentiable rendering layer
with screen-space ray tracing, resulting in more realistic specular reflections
that match the input photo. In addition, we create …
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