We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement. The method first samples the surface using non-differentiable rasterization, then applies differentiable, depth-aware point splatting to produce the final image. Our approach requires no differentiable meshing or rasterization steps, making it efficient for large 3D models and applicable to isosurfaces extracted from implicit surface definitions. We demonstrate the effectiveness of our method for implicit-, mesh-, and parametric-surface-based inverse rendering and neural-network training applications. In particular, we show for the first time efficient, differentiable rendering of an isosurface extracted from a neural radiance field (NeRF), and demonstrate surface-based, rather than volume-based, rendering of a NeRF.

提出一种能够支持显式和隐式表示，可在遮挡边界提供导数的3D表面的可微渲染方法，使用非可微光栅化对表面进行采样，然后应用可微的深度感知的点喷洒技术生成最终图像，适用于大型3D模型和隐式表面定义提取的等值面的应用。

通过非可微取样的可微曲面渲染