Xu, XiaofengWang, LuWang, BeibeiWilkie, Alexander2025-06-202025-06-202025978-3-03868-292-91727-3463https://doi.org/10.2312/sr.20251184https://diglib.eg.org/handle/10.2312/sr20251184Monte Carlo integration estimates the path integral in light transport by randomly sampling light paths and averaging their contributions. However, in scenes with many lights, the resulting estimates suffer from noise and slow convergence due to highfrequency discontinuities introduced by complex light visibility, scattering functions, and emissive properties. To mitigate these challenges, control variates have been employed to approximate the integrand and reduce variance. While previous approaches have shown promise in direct illumination application, they struggle to efficiently handle the discontinuities inherent in manylight environments, especially when relying on a single control variate. In this work, we introduce an adaptive method that generates multiple control variates tailored to the spatial distribution and number of lights in the scene. Drawing inspiration from hierarchical light clustering methods like Lightcuts, our approach dynamically determines the number of control variates. We validate our method on the direct illumination problem in scenes with many lights, demonstrating that our adaptive multiple control variates not only outperform single control variate strategy but also achieve a modest improvement over current stateof- the-art many-light sampling techniques.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> RenderingComputing methodologiesRendering10.2312/sr.2025118412 pages