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.

基于光栅化的可微分渲染器方法，通过微边缘的引入，解决了可视性不连续的梯度计算问题，简化了传统复杂问题，实现了直接、有效且高效的解决方案。该方法适用于光栅化掩码、深度和法线图像，可简化不连续处的梯度解释，并处理几何交叉点，相较于先前技术具有优势。在动态人头场景重建中展示了方法，在处理相机图像和分割掩码方面表现出效果。

光栅化边缘梯度：可微分处理不连续性

Rasterized Edge Gradients: Handling Discontinuities Differentiably

The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered
methodology, entangling the aspects of illumination and material reflectance
into emission solely from 3D points. This simplified rendering approach
presents challenges in accurately modeling images captured under adverse
lighting conditions, such as low light or over-exposure. Motivated by the
ancient Greek emission theory that posits visual perception as a result of rays
emanating from the eyes, we slightly refine the conventional NeRF framework to
train NeRF under challenging light conditions and generate normal-light
condition novel views unsupervised. We introduce the concept of a "Concealing
Field," which assigns transmittance values to the surrounding air to account
for illumination effects. In dark scenarios, we assume that object emissions
maintain a standard lighting level but are attenuated as they traverse the air
during the rendering process. Concealing Field thus compel NeRF to learn
reasonable density and colour estimations for objects even in dimly lit
situations. Similarly, the Concealing Field can mitigate over-exposed emissions
during the rendering stage. Furthermore, we present a comprehensive multi-view
dataset captured under challenging illumination conditions for evaluation. Our
code and dataset available at this https URL

通过引入 “Concealing Field” 的概念，我们在挑战性的光照条件下训练和生成正常光照情况下的新视角，迭代了传统的 NeRF 框架，并通过模拟空气中的传输效果，实现了在昏暗情况下对象的合理密度和颜色估计。