Guided Lens Sampling for Efficient Monte Carlo Circle-of-Confusion Rendering

We introduce a guided lens sampling method for efficient rendering of circles of confusion (CoCs). While traditional Monte Carlo techniques simulate depth-of-field (DoF) effects by perturbing camera rays at the lens, uniform lens sampling often results in significant noise by failing to prioritize rays toward highlight regions in the scene. Although path guiding has proven effective for global illumination by learning importance distributions for incoming radiance, no comparable guiding technique for CoCs exists, primarily due to the strong parallax between adjacent pixels. We model highlight spots in world space using a globally shared radiance field, which is then transformed into lens space through a bipolar-cone projection to guide camera ray generation. We implement this theory using 3D Gaussians, achieving fast, robust guiding with minimal computational and storage overhead, making it suitable for production rendering. We also propose two extensions to further enhance local adaptation. Our experiments show that this approach significantly improves the sampling efficiency for CoC rendering.