Lune

SIGGRAPH2022Top-tier venue

Efficient estimation of boundary integrals for path-space differentiable rendering

Kai Yan, Christoph Lassner, Brian Budge, Zhao Dong, Shuang Zhao

2022Year
34Citations
5Top-tier citations

Abstract

Boundary integrals are unique to physics-based differentiable rendering and crucial for differentiating with respect to object geometry. Under the differential path integral framework---which has enabled the development of sophisticated differentiable rendering algorithms---the boundary components are themselves path integrals. Previously, although the mathematical formulation of boundary path integrals have been established, efficient estimation of these integrals remains challenging. In this paper, we introduce a new technique to efficiently estimate boundary path integrals. A key component of our technique is a primary-sample-space guiding step for importance sampling of boundary segments. Additionally, we show multiple importance sampling can be used to combine multiple guided samplings. Lastly, we introduce an optional edge sorting step to further improve the runtime performance. We evaluate the effectiveness of our method using several differentiable-rendering and inverse-rendering examples and provide comparisons with existing methods for reconstruction as well as gradient quality.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext 44faa375-0e8e-42c4-aae6-ff2d48fa9091

Cited by top-tier papers5

Ask how each one uses it

Builds on2

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines