We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines. To tackle visibility-related derivatives that make rendering non-differentiable, existing physically based differentiable rendering methods often rely on elaborate guiding data structures or reparameterization with a global impact on variance. In this article, we investigate an alternative that embraces nonzero bias in exchange for low variance and architectural simplicity. Our method expands the lower-dimensional boundary integral into a thin band that is easy to sample when the underlying surface is represented by an SDF. We demonstrate the performance and robustness of our formulation in end-to-end inverse rendering tasks, where it obtains results that are competitive with or superior to existing work.

翻译：我们提出了一种简单算法，用于实现由符号距离场（Signed Distance Fields, SDF）表示的曲面的可微分渲染，使得将渲染集成到基于梯度的优化流程中变得简便。为解决导致渲染不可微的可见性相关导数问题，现有的基于物理的可微分渲染方法往往依赖于复杂的引导数据结构或具有全局方差影响的重参数化技术。本文探索了一种替代方案，通过引入非零偏差来换取低方差和架构简洁性。当底层曲面由SDF表示时，我们的方法将低维边界积分扩展为易于采样的薄带结构。我们在端到端逆渲染任务中验证了所提方法的性能与鲁棒性，其获得的结果与现有工作相比具有竞争力或更优。