Physics-based differentiable rendering: from theory to implementation
Physics-based differentiable rendering: from theory to implementation
复制标题
基于物理的可微渲染：从理论到实现
DOI：
10.1145/3388769.3407454
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Tzu
中科院分区：
文献类型：
--
作者：
Shuang Zhao;Wenzel Jakob;Tzu
基于物理的渲染算法通过虚拟场景的详细数学表示模拟光的流动来生成真实感图像。相比之下，基于物理的可微分渲染算法专注于计算表现出复杂光传输效果的图像的导数（例如，软阴影、相互反射和焦散）相对于任意场景参数，诸如相机姿态，对象几何形状（例如，顶点位置）以及表示为2D纹理和3D体积的空间变化的材料属性。这种新的通用性水平使得基于物理的可微分渲染成为解决许多具有挑战性的逆渲染问题的关键因素，即使用基于梯度的方法搜索优化用户指定目标函数的场景配置（如下图所示）。此外，这些技术可以并入概率推理和机器学习流水线中。例如，可微分渲染器允许利用捕获的复杂光传输效果来计算“渲染损失”。此外，它们还可以用作合成真实感图像的生成模型。
Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms focus on computing derivative of images exhibiting complex light transport effects (e.g., soft shadows, interreflection, and caustics) with respect to arbitrary scene parameters such as camera pose, object geometry (e.g., vertex positions) as well as spatially varying material properties expressed as 2D textures and 3D volumes. This new level of generality has made physics-based differentiable rendering a key ingredient for solving many challenging inverse-rendering problems, that is, the search of scene configurations optimizing user-specified objective functions, using gradient-based methods (as illustrated in the figure below). Further, these techniques can be incorporated into probabilistic inference and machine learning pipelines. For instance, differentiable renderers allow "rendering losses" to be computed with complex light transport effects captured. Additionally, they can be used as generative models that synthesize photorealistic images.