Computing the gradients of a rendering process is paramount for diverse applications in computer vision and graphics. However, accurate computation of these gradients is challenging due to discontinuities and rendering approximations, particularly for surface-based representations and rasterization-based rendering. We present a novel method for computing gradients at visibility discontinuities for rasterization-based differentiable renderers. Our method elegantly simplifies the traditionally complex problem through a carefully designed approximation strategy, allowing for a straightforward, effective, and performant solution. We introduce a novel concept of micro-edges, which allows us to treat the rasterized images as outcomes of a differentiable, continuous process aligned with the inherently non-differentiable, discrete-pixel rasterization. This technique eliminates the necessity for rendering approximations or other modifications to the forward pass, preserving the integrity of the rendered image, which makes it applicable to rasterized masks, depth, and normals images where filtering is prohibitive. Utilizing micro-edges simplifies gradient interpretation at discontinuities and enables handling of geometry intersections, offering an advantage over the prior art. We showcase our method in dynamic human head scene reconstruction, demonstrating effective handling of camera images and segmentation masks.

翻译：计算渲染过程的梯度对于计算机视觉和图形学中的多种应用至关重要。然而，由于不连续性和渲染近似（特别是基于表面的表示和基于光栅化的渲染），这些梯度的精确计算面临挑战。我们提出了一种新颖方法，用于计算基于光栅化的可微分渲染器中可见性不连续处的梯度。该方法通过精心设计的近似策略，优雅地简化了传统上复杂的问题，提供了一种直接、有效且高性能的解决方案。我们引入了微边缘这一新概念，使得光栅化图像可被视为可微分连续过程的结果，而与本质上不可微分离散像素的光栅化保持一致。该技术无需对前向传递进行渲染近似或其他修改，保持了渲染图像的完整性，从而适用于无法进行滤波的光栅化掩模、深度和法线图像。利用微边缘简化了不连续处的梯度解释，并支持几何交点的处理，相较于现有技术具有优势。我们在动态人头部场景重建中展示了该方法，有效处理了相机图像和分割掩模。