Bidirectional path tracing (BPT) with multiple importance sampling (MIS) is a popular technique for rendering realistic images. Recently, it has been shown that BPT can be improved by preparing multiple light sub-paths and by resampling a small number of light sub-paths from them to generate full paths with large contribution. Traditionally, for MIS weights, the balance heuristic has widely been used to minimize the upper bound of variance, where each full path is weighted in proportion to the probability of the path. Although the probability of the path can change due to the resampling process, the weighting functions used in the previous methods remain unaffected by the change in probability, resulting in less efficiency. To address this problem, we propose new weighting functions for BPT with multiple light sub-paths. Our main contribution is a precise formulation of the variance and the derivation of the weighting functions that can appropriately treat the change in probability. We demonstrate that our weighting functions significantly improve the image quality. We will release a simple version of our implementation as open source to ensure reproducibility.

Figure 1: Rendering results of (a) our method, relative error images of (b) our method and (c) Lightcuts [WFA * 05] in Sponza scene. Relative error threshold ε = 2% and confidence level α = 95% are specified. 92.96% pixels satisfy that the relative error is within 2% in our method, while only 43.67% pixels satisfy the condition in Lightcuts. In (d) Kitchen scene and (e) San Miguel scene, Cook-Torrance BRDFs and Ashikhmin-Shirely BRDFs are used, which cannot be used in Lightcuts. (f) and (g) show relative error images of (d) and (e), respectively. AbstractThe popularity of many-light rendering, which converts complex global illumination computations into a simple sum of the illumination from virtual point lights (VPLs), for predictive rendering has increased in recent years. A huge number of VPLs are usually required for predictive rendering at the cost of extensive computational time. While previous methods can achieve significant speedup by clustering VPLs, none of these previous methods can estimate the total errors due to clustering. This drawback imposes on users tedious trial and error processes to obtain rendered images with reliable accuracy. In this paper, we propose an error estimation framework for many-light rendering. Our method transforms VPL clustering into stratified sampling combined with confidence intervals, which enables the user to estimate the error due to clustering without the costly computing required to sum the illumination from all the VPLs. Our estimation framework is capable of handling arbitrary BRDFs and is accelerated by using visibility caching, both of which make our method more practical. The experimental results demonstrate that our method can estimate the error much more accurately than the previous clustering method.

Divide-and-conquer ray tracing (DACRT) methods solve intersection problems between large numbers of rays and primitives by recursively subdividing the problem size until it can be easily solved. Previous DACRT methods subdivide the intersection problem based on the distribution of primitives only, and do not exploit the distribution of rays, which results in a decrease of the rendering performance especially for high resolution images with antialiasing. We propose an efficient DACRT method that exploits the distribution of rays by sampling the rays to construct an acceleration data structure. To accelerate ray traversals, we have derived a new cost metric which is used to avoid inefficient subdivision of the intersection problem where the number of rays is not sufficiently reduced. Our method accelerates the tracing of many types of rays (primary rays, less coherent secondary rays, random rays for path tracing) by a factor of up to 2 using ray sampling.

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In many-light rendering, a variety of visual and illumination effects, including anti-aliasing, depth of field, volumetric scattering, and subsurface scattering, are combined to create a number of virtual point lights (VPLs). This is done in order to simplify computation of the resulting illumination. Naive approaches that sum the direct illumination from many VPLs are computationally expensive; scalable methods can be computed more efficiently by clustering VPLs, and then estimating their sum by sampling a small number of VPLs. Although significant speed-up has been achieved using scalable methods, clustering leads to uncontrollable errors, resulting in noise in the rendered images. In this paper, we propose a method to improve the estimation accuracy of manylight rendering involving such visual and illumination effects. We demonstrate that our method can improve the estimation accuracy by a factor of 2.3 over the previous method.