  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.
