Rich‐VPLs for Improving the Versatility of Many‐Light Methods

It is still challenging to render directional but non‐specular reflections in complex scenes. The SG‐based (Spherical Gaussian) many‐light framework provides a scalable solution but still requires a large number of glossy virtual lights to avoid spikes as well as reduce clamping errors. Directly gathering contributions from these glossy virtual lights to each pixel in a pairwise way is very inefficient. In this paper, we propose an adaptive algorithm with tighter error bounds to efficiently compute glossy interreflections from glossy virtual lights. This approach is an extension of the Lightcuts that builds hierarchies on both lights and pixels with new error bounds and new GPU‐based traversal methods between light and pixel hierarchies. Results demonstrate that our method is able to faithfully and efficiently compute glossy interreflections in scenes with highly glossy and spatial varying reflectance. Compared with the conventional Lightcuts method, our approach generates lightcuts with only one‐fourth to one‐fifth light nodes therefore exhibits better scalability. Additionally, after being implemented on GPU, our algorithms achieve a magnitude of faster performance than the previous method.

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spherical Gaussian‐based Lightcuts for Glossy Interreflections

Huo

Jin

Liu

et al. 2020

“…Keller introduced the instant radiosity method, which calculates the indirect illumination by using virtual point lights (VPLs) [Kel97]. Following this seminal work, a number of many‐light methods that can handle highly glossy materials [HKWB09, DKH*10, SHD15] and can achieve interactive rendering [RGK*08, Tok15], have been proposed.…”

Section: Previous Workmentioning

confidence: 99%

An Error Estimation Framework for Many‐Light Rendering

Nabata

Iwasaki

Dobashi

et al. 2016

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.

“…Several limitations of VPL‐based algorithms can be addressed, e.g. clamping [KK06] or diffuse only surface BRDFs by using virtual spherical lights [HKWB09] (VSL) or Rich‐VPLs [SHD15]. To cope better with highly glossy material [KFB10], Davidovič et al .…”

Section: Previous Workmentioning

confidence: 99%

Global Illumination Using Well‐Separated Pair Decomposition

Bus

Mustafa

Biri

2015

International audienceInstant radiosity methods rely on using a large number of virtual point lights (VPLs) to approximate global illumination. Efficiency considerations require grouping the VPLs into a small number of clusters that are treated as individual lights with respect to each point to be shaded. Two examples of clustering algorithms are Light-cuts [WFA * 05] and LightSlice [OP11]. In this work we use the notion of geometric separatedness of point sets as a basis for a data structure for pre-computing and compactly storing a set of candidate VPL clusterings. Our data structure is created prior to rendering, is view-independent and relies only on geometric and radiometric information. For any point to be shaded, we show that a suitable clustering of the VPLs can be efficiently extracted from this data structure. We develop the above framework into an accurate and efficient clustering algorithm based on well-separated pair decompositions which outperforms earlier work in speed and/or quality for diffuse scenes