基于物理的可微渲染综述

邢健开; 徐昆

doi:10.11834/jig.230715

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专辑

基于物理的可微渲染综述
Physically based differentiable rendering： a survey
2024年29卷第10期页码：2912-2925
收稿：2023-10-07，

修回：2024-02-06，

纸质出版：2024-10-16
DOI： 10.11834/jig.230715
稿件说明：

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邢健开，徐昆. 2024. 基于物理的可微渲染综述. 中国图象图形学报， 29(10):2912-2925 DOI： 10.11834/jig.230715.

Xing Jiankai， Xu Kun. 2024. Physically based differentiable rendering： a survey. Journal of Image and Graphics， 29(10):2912-2925 DOI： 10.11834/jig.230715.

摘要

可微渲染技术是当前计算机图形学与计算机视觉方向的研究热点，其目标是将计算机图形学中的渲染流水线进行可微化改造以支持计算渲染的输出图像关于输入参数如几何、材质等参数的梯度。结合渲染图像与目标图像之间的损失函数，可微渲染允许在生成式分析的框架中，通过梯度下降的方式从图像中推理出场景参数，是解决三维重建、逆向渲染等领域问题的有效方法，并在虚拟现实、自动驾驶等领域有着广泛的应用前景。基于物理的可微渲染旨在对基于物理的渲染管线进行可微化改造，主要涉及对场景几何和材质的表达，以及光路传输模拟过程的梯度计算方法。本文对近年来基于物理的可微渲染领域的发展情况进行了调研，总结了基于物理的可微渲染研究进展。首先总体介绍正向渲染和可微渲染的计算方法；然后介绍如何针对具体的几何、材质以及相机的表达方式进行梯度计算；讨论如何提高可微渲染的效率和鲁棒性；展示可微渲染如何应用于实际任务中；最后本文展望了可微渲染的发展趋势，期望推动该领域的进一步发展。

Abstract

Rendering has been a prominent subject in the field of computer graphics for an extended period. It can be regarded as a function that accepts an abstract scene description as input and typically generates a 2D image as output. The theory and practice of rendering have remarkably advanced through years of research. In recent years， inverse rendering has emerged as a new research focus in the field of computer graphics due to the development of digital technology. The objective of inverse rendering is to reverse the rendering process and deduce scene parameters from the output image， which is equivalent to solving the inverse function of the rendering function. This process plays a crucial role in addressing perception problems in diverse advanced technological domains， including virtual reality， autonomous driving， and robotics. Numerous methods exist for implementing inverse rendering， with the current mainstream framework being optimization through “analysis by synthesis”. First， it estimates a set of initial scene parameters， then performs forward rendering on the scene， compares the rendered result with the target image， and then minimizes the difference （loss function） by optimizing the scene parameters using gradient descent-based method. This pipeline necessitates the ability to compute the derivatives of the output image in forward rendering with respect to the input parameters. Consequently， differentiable rendering has emerged to fulfill this requirement. Specifically， the research topic of differentiable rendering is to convert the forward rendering pipeline in computer graphics into a differentiable form， enabling the differentiation of the output image with respect to input parameters such as geometry， material， light source， and camera. Currently， forward rendering can be broadly categorized into three types： rasterization-based rendering， physically based rendering， and the emerging neural rendering. Rasterization-based rendering is a fundamental technique in computer graphics that converts geometric shapes into pixels for display. It involves projecting 3D objects onto a 2D screen， performing hidden surface removal， shading， and texturing to create realistic images efficiently. While rasterization is fast and suitable for real-time applications， it may lack physical accuracy in simulating light interactions. By contrast， physically based rendering aims to simulate real-world light behavior accurately by considering the physical properties of materials， light sources， and the environment. It calculates how light rays interact with surfaces， accounting for reflections， refractions， and scattering to produce photorealistic visual results. This method prioritizes realism and is widely used in industries， such as animation， gaming， and visual effects. Neural rendering is an emerging rendering technique in recent years， mainly used for image-based rendering tasks. In contrast to traditional graphics rendering， image-based rendering does not require any explicit 3D scene information （geometry， materials， lighting， etc.）， but instead implicitly encodes scenes through a sequence of 2D images sampled from different viewpoints， enabling the generation of images of the scene from any viewpoint. Accordingly， differentiable rendering can also be categorized into three types： differentiable rasterization， physically based differentiable rendering， and differentiable neural rendering. In differentiable rasterization， many works employ approximate methods to compute approximate derivatives of the rasterization process for backpropagation of gradients or modify steps in the traditional rendering pipeline （usually rasterization and testing/blending steps） to make pixels differentiable with respect to vertices. Neural rendering is naturally differentiable because its rendering process is conducted through neural networks. For physically based differentiable rendering， accurately calculating the gradient of the image concerning scene parameters is challenging because of the intricate nature of geometry， material， and light transmission processes. Therefore， this study concentrates on recent research in the field of physically based differentiable rendering. The article is organized into the following sections： Section 1 introduces the computational methods of forward rendering and differentiable rendering from an abstract standpoint and two types of method for correctly computing boundary integral： edge sampling and reparameterization. Section 2 explores the attainment of differentiable rendering for distinct representations of geometry， such as volumetric representation， signed distance field， height field， and vectorized geometry； materials， such as volumetric material； parameterized bidirectional reflectance distribution function， bidirectional surface scattering reflectance distribution function， and continuously varying refractive index fields； and camera-related parameters， such as pixel reconstruction filter and time-of-flight camera. Section 3 focuses on enhancing the efficiency and robustness of differentiable rendering， including efficient sampling， high-efficiency system and framework， language for differentiable rendering and several techniques， to enhance the robustness of differentiable rendering. Section 4 showcases the application of differentiable rendering in practical tasks， which can be generally divided in three types： single-object reconstruction， object and environment light reconstruction， and scene reconstruction. Section 5 discusses the future development trends of differentiable rendering， including improving efficiency， robustness of differentiable rendering， and combining differentiable rendering with other methods.

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