Year: 2024
Type: conference-paper
Abstract: 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 visibilityrelated 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.
Source: SIGGRAPH Asia Conference Papers
Institutions: Cornell University, École Polytechnique Fédérale de Lausanne
Language: English
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404965515