RenderMan

Author:

Christensen Per1,Fong Julian1,Shade Jonathan1,Wooten Wayne1,Schubert Brenden1,Kensler Andrew1,Friedman Stephen1,Kilpatrick Charlie1,Ramshaw Cliff1,Bannister Marc1,Rayner Brenton1,Brouillat Jonathan1,Liani Max1

Affiliation:

1. Pixar Animation Studios

Abstract

Pixar’s RenderMan renderer is used to render all of Pixar’s films and by many film studios to render visual effects for live-action movies. RenderMan started as a scanline renderer based on the Reyes algorithm, and it was extended over the years with ray tracing and several global illumination algorithms. This article describes the modern version of RenderMan, a new architecture for an extensible and programmable path tracer with many features that are essential to handle the fiercely complex scenes in movie production. Users can write their own materials using a bxdf interface and their own light transport algorithms using an integrator interface—or they can use the materials and light transport algorithms provided with RenderMan. Complex geometry and textures are handled with efficient multi-resolution representations, with resolution chosen using path differentials. We trace rays and shade ray hit points in medium-sized groups, which provides the benefits of SIMD execution without excessive memory overhead or data streaming. The path-tracing architecture handles surface, subsurface, and volume scattering. We show examples of the use of path tracing, bidirectional path tracing, VCM, and UPBP light transport algorithms. We also describe our progressive rendering for interactive use and our adaptation of denoising techniques.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Reference116 articles.

1. An adaptive point sampler on a regular lattice

2. Anthony Apodaca and Larry Gritz. 2000. Advanced RenderMan: Creating CGI for Motion Pictures. Morgan Kaufmann. Anthony Apodaca and Larry Gritz. 2000. Advanced RenderMan: Creating CGI for Motion Pictures. Morgan Kaufmann.

3. Particle transport and image synthesis

4. Kernel-predicting convolutional networks for denoising Monte Carlo renderings

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