论文标题
高性能区分渲染的模块化原始图
Modular Primitives for High-Performance Differentiable Rendering
论文作者
论文摘要
我们提出了一个模块化的渲染器设计，该设计通过利用现有的高度优化的硬件图形管道来使性能优于以前的方法。我们的设计支持现代图形管道中的所有关键操作：将大量三角形，属性插值，过滤的纹理查找以及用户可编程的阴影和几何处理处理，所有这些都在高分辨率中。我们的模块化原始图允许在自动分化框架（例如Pytorch或Tensorflow）中直接构建自定义的高性能图形管道。作为一种激励性的应用，我们将面部绩效捕获作为反向渲染问题，并表明可以使用我们的工具有效地解决它。我们的结果表明，这种简单明了的方法在渲染结果和参考图像之间实现了出色的几何对应关系。
We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a modern graphics pipeline: rasterizing large numbers of triangles, attribute interpolation, filtered texture lookups, as well as user-programmable shading and geometry processing, all in high resolutions. Our modular primitives allow custom, high-performance graphics pipelines to be built directly within automatic differentiation frameworks such as PyTorch or TensorFlow. As a motivating application, we formulate facial performance capture as an inverse rendering problem and show that it can be solved efficiently using our tools. Our results indicate that this simple and straightforward approach achieves excellent geometric correspondence between rendered results and reference imagery.
