PBR-Net: Imitating Physically Based Rendering using Deep Neural Network
Peng Dai Zhuwen Li Yinda Zhang Shuaicheng Liu Bing Zeng
University of Electronic Science and Technology of China
Google Research




Physically based rendering has been widely used to generate photo-realistic images, which greatly impacts industry by providing appealing rendering, such as for entertainment and augmented reality, and academia by serving large scale highfidelity synthetic training data for data hungry methods like deep learning. However, physically based rendering heavily relies on ray-tracing, which can be computational expensive in complicated environment and hard to parallelize. In this paper, we propose an end-to-end deep learning based approach to generate physically based rendering efficiently. Our system consists of two stacked neural networks, which effectively simulates the physical behavior of the rendering process and produces photo-realistic images. The first network, namely shading network, is designed to predict the optimal shading image from surface normal, depth and illumination; the second network, namely composition network, learns to combine the predicted shading image with the reflectance to generate the final result. Our approach is inspired by intrinsic image decomposition, and thus it is more physically reasonable to have shading as intermediate supervision. Extensive experiments show that our approach is robust to noise thanks to a modified perceptual loss and even outperforms the physically based rendering systems in complex scenes given a reasonable time budget.




"PBR-Net: Imitating Physically Based Rendering using Deep Neural Network",
Peng Dai, Zhuwen Li, Yinda Zhang, Shuaicheng Liu, Bing Zeng
IEEE Transactions on Image Processing (TIP), 2020

[paper] [arxiv]



Network architecture and workflow. Our network mainly consists of two sub-networks, i.e. shading network and composition network. Shading network synthesizes shading image using surface normal, depth, panoramic illumination (distance and intensity) as inputs. Composition network combines the generated shading image with the reflectance to produce a color image.



(a)Transform directly using U-Net. (b)Shading images from PBR-Net. (c)Color images from PBR-Net. (d)Ground truth.

Test results on pbrs[1]. First and second columes are ground truth using mitsuba. Third and fourth columes are predictions from PBR-Net.

Test results on public avaiable models[2] after finetuning. The first and second rows are ground truth using mitsuba. Third and fourth rows are predictions from PBR-Net. Note that the scene for test is excluded from the finetune set .



(1) Due to the limitation of high quality models, the generated results have a gap with photo-realistic images. In the future, with the development of inverse rendering and more public avaiable models. Eventually, the rendering results will go further.
(2) In this implementation, the panaromic illumination images only encode the transparent windows, doors and self-emmitting objects in one room as light sources. What's more, how to effectively insert the illumination information into network to better relight the scene is also a valuable direction in the furture, especifically for complex scenes with multiple rooms.



[1] Y. Zhang, S. Song, E. Yumer, M. Savva, J.-Y. Lee, H. Jin, and T. Funkhouser, “Physically-based rendering for indoor scene understanding using convolutional neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 5057–5065.
[2] B. Bitterli, “Rendering resources,” 2016, https://benediktbitterli.me/resources/.


Last updated: July 2020