Path-space differentiable rendering
Path-space differentiable rendering
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DOI：
10.1145/3386569.3392383
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Cheng Zhang;Bailey Miller;Kai Yan;Ioannis Gkioulekas;Shuang Zhao
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文献类型：
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作者：
Cheng Zhang;Bailey Miller;Kai Yan;Ioannis Gkioulekas;Shuang Zhao
基于物理的可微渲染，相对于任意场景参数的辐射测量的导数的估计，具有从解决综合分析问题到训练包含前向渲染过程的机器学习管道的各种应用。不幸的是，通用可微渲染仍然具有挑战性，由于缺乏有效的估计，以及需要识别和处理复杂的不连续性，如可见性边界。在本文中，我们展示了如何路径积分可以区分相对于任意可微变化的场景。我们提供了一个详细的理论分析，这一过程，并建立新的微分渲染配方的基础上产生的差分路径积分。我们的路径空间可微渲染公式允许设计新的蒙特卡罗估计器，在处理复杂的几何不连续性和光传输现象（如焦散）方面，该估计器比最先进的方法提供更好的效率。我们验证我们的方法，通过比较我们的衍生估计使用有限差分法产生的。为了证明我们的技术的有效性，我们比较了逆渲染性能与一些国家的最先进的微分渲染方法。
Physics-based differentiable rendering, the estimation of derivatives of radiometric measures with respect to arbitrary scene parameters, has a diverse array of applications from solving analysis-by-synthesis problems to training machine learning pipelines incorporating forward rendering processes. Unfortunately, general-purpose differentiable rendering remains challenging due to the lack of efficient estimators as well as the need to identify and handle complex discontinuities such as visibility boundaries. In this paper, we show how path integrals can be differentiated with respect to arbitrary differentiable changes of a scene. We provide a detailed theoretical analysis of this process and establish new differentiable rendering formulations based on the resulting differential path integrals. Our path-space differentiable rendering formulation allows the design of new Monte Carlo estimators that offer significantly better efficiency than state-of-the-art methods in handling complex geometric discontinuities and light transport phenomena such as caustics. We validate our method by comparing our derivative estimates to those generated using the finite-difference method. To demonstrate the effectiveness of our technique, we compare inverse-rendering performance with a few state-of-the-art differentiable rendering methods.