Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire's lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for evaluating the complex luminaire at a specific point and view direction, importance sampling a point on the luminaire given a shading location, and blending to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.

Simulating a complex luminaire such as a chandelier is expensive and slow, even using state-of-the-art algorithms. A more practical alternative is to use precomputation to accelerate rendering. Prior approaches cached information on an aperture surface that separates the luminaire from the scene, but many luminaires have large or ill-defined apertures leading to excessive data storage and inaccurate results. In this article, we separate luminaire rendering into illumination and appearance components. A precomputation stage simulates the complex light flow inside the luminaire to generate two data structures: a set of anisotropic point lights (APLs) and a radiance volume. The APLs are located near apparent sources and represent the light leaving the luminaire, allowing its nearand far-field illumination to be accurately and efficiently computed at render time. The luminaire's appearance consists of high- and low-frequency components, which are both visually important. High-frequency components are computed dynamically at render time, while the more computationally expensive low-frequency components are approximated using the precomputed radiance volume. Results are shown for several complex luminaires, demonstrating orders of magnitude faster rendering compared to the best global illumination algorithms and higher fidelity with greatly reduced storage requirements compared to previous precomputed approaches.

This paper describes a technique to automatically adapt programmable shaders for use in physically-based rendering algorithms. Programmable shading provides great flexibility and power for creating rich local material detail, but only allows the material to be queried in one limited way: point sampling. Physically-based rendering algorithms simulate the complex global flow of light through an environment but rely on higher level information about the material properties, such as importance sampling and bounding, to intelligently solve high dimensional rendering integrals. We propose using a compiler to automatically generate interval versions of programmable shaders that can be used to provide the higher level query functions needed by physically-based rendering without the need for user intervention or expertise. We demonstrate the use of programmable shaders in two such algorithms, multidimensional lightcuts and photon mapping, for a wide range of scenes including complex geometry, materials and lighting.

Abstract Rendering animations of scenes with deformable objects, camera motion, and complex illumination, including indirect lighting and arbitrary shading, is a long‐standing challenge. Prior work has shown that complex lighting can be accurately approximated by a large collection of point lights. In this formulation, rendering of animation sequences becomes the problem of efficiently shading many surface samples from many lights across several frames. This paper presents a tensor formulation of the animated many‐light problem, where each element of the tensor expresses the contribution of one light to one pixel in one frame. We sparsely sample rows and columns of the tensor, and introduce a clustering algorithm to select a small number of representative lights to efficiently approximate the animation. Our algorithm achieves efficiency by reusing representatives across frames, while minimizing temporal flicker. We demonstrate our algorithm in a variety of scenes that include deformable objects, complex illumination and arbitrary shading and show that a surprisingly small number of representative lights is sufficient for high quality rendering. We believe out algorithm will find practical use in applications that require fast previews of complex animation.