1.7 A Brief History of Physically Based Rendering

Through the early years of computer graphics in the 1970s, the most important problems to solve were fundamental issues like visibility algorithms and geometric representations. When a megabyte of RAM was a rare and expensive luxury and when a computer capable of a million floating-point operations per second cost hundreds of thousands of dollars, the complexity of what was possible in computer graphics was correspondingly limited, and any attempt to accurately simulate physics for rendering was infeasible.

As computers have become more capable and less expensive, it became possible to consider more computationally demanding approaches to rendering, which in turn has made physically based approaches viable. This progression is neatly explained by Blinn’s law: “as technology advances, rendering time remains constant.”

Jim Blinn’s simple statement captures an important constraint: given a certain number of images that must be rendered (be it a handful for a research paper or over a hundred thousand for a feature film), it’s only possible to take so much processing time for each one. One has a certain amount of computation available and one has some amount of time available before rendering must be finished, so the maximum computation per image is necessarily limited.

Blinn’s law also expresses the observation that there remains a gap between the images people would like to be able to render and the images that they can render: as computers have become faster, content creators have continued to use increased computational capability to render more complex scenes with more sophisticated rendering algorithms, rather than rendering the same scenes as before, just more quickly. Rendering continues to consume all of the computational capabilities made available to it.

1.7.1 Research

Physically based approaches to rendering started to be seriously considered by graphics researchers in the 1980s. Whitted’s paper (1980) introduced the idea of using ray tracing for global lighting effects, opening the door to accurately simulating the distribution of light in scenes. The rendered images his approach produced were markedly different from any that had been seen before, which spurred excitement about this approach.

Another notable early advancement in physically based rendering was Cook and Torrance’s reflection model (1981, 1982), which introduced microfacet reflection models to graphics. Among other contributions, they showed that accurately modeling microfacet reflection made it possible to render metal surfaces accurately; metal was not well rendered by earlier approaches.

Shortly afterward, Goral et al. (1984) made connections between the thermal transfer literature and rendering, showing how to incorporate global diffuse lighting effects using a physically based approximation of light transport. This method was based on finite-element methods, where areas of surfaces in the scene exchanged energy with each other. This approach came to be referred to as “radiosity,” after a related physical unit. Following work by Cohen and Greenberg (1985) and Nishita and Nakamae (1985) introduced important improvements. Once again, a physically based approach led to images with lighting effects that hadn’t previously been seen in rendered images, which led to many researchers pursuing improvements in this area.

While the radiosity approach was strongly based on physical units and conservation of energy, in time it became clear that it didn’t lead to viable rendering algorithms: the asymptotic computational complexity was a difficult-to-manage upper O left-parenthesis n squared right-parenthesis , and it was necessary to be able to re-tessellate geometric models along shadow boundaries for good results; researchers had difficulty developing robust and efficient tessellation algorithms for this purpose and radiosity’s adoption in practice was limited.

During the radiosity years, a small group of researchers pursued physically based approaches to rendering that were based on ray tracing and Monte Carlo integration. At the time, many looked at their work with skepticism; objectionable noise in images due to variance from Monte Carlo integration seemed unavoidable, while radiosity-based methods quickly gave visually pleasing results, at least on relatively simple scenes.

In 1984, Cook, Porter, and Carpenter introduced distributed ray tracing, which generalized Whitted’s algorithm to compute motion blur and defocus blur from cameras, blurry reflection from glossy surfaces, and illumination from area light sources (Cook et al. 1984), showing that ray tracing was capable of generating a host of important lighting effects.

Shortly afterward, Kajiya (1986) introduced path tracing; he set out a rigorous formulation of the rendering problem (the light transport integral equation) and showed how to apply Monte Carlo integration to solve it. This work required immense amounts of computation: to render a 256 times 256 pixel image of two spheres with path tracing required 7 hours of computation on an IBM 4341 computer, which cost roughly $280,000 when it was first released (Farmer 1981). With von Herzen, Kajiya also introduced the volume-rendering equation to graphics (Kajiya and von Herzen 1984); this equation rigorously describes the scattering of light in participating media.

Both Cook et al.’s and Kajiya’s work once again led to images unlike any that had been seen before, demonstrating the value of physically based methods. In subsequent years, important work on Monte Carlo for realistic image synthesis was described in papers by Arvo and Kirk (1990) and Kirk and Arvo (1991). Shirley’s Ph.D. dissertation (1990) and follow-on work by Shirley et al. (1996) were important contributions to Monte Carlo–based efforts. Hall’s book, Illumination and Color in Computer Generated Imagery, (1989) is one of the first books to present rendering in a physically based framework, and Andrew Glassner’s Principles of Digital Image Synthesis rigorously laid out foundations of the field (1995). Ward’s Radiance rendering system was an early open source physically based rendering system, focused on lighting design (Ward 1994), and Slusallek’s Vision renderer was designed to bridge the gap between physically based approaches and the then widely used RenderMan interface, which wasn’t physically based (Slusallek 1996).

Following Torrance and Cook’s work, much of the research in the Program of Computer Graphics at Cornell University investigated physically based approaches. The motivations for this work were summarized by Greenberg et al. (1997), who made a strong argument for a physically accurate rendering based on measurements of the material properties of real-world objects and on deep understanding of the human visual system.

A crucial step forward for physically based rendering was Veach’s work, described in detail in his dissertation (Veach 1997). Veach advanced key theoretical foundations of Monte Carlo rendering while also developing new algorithms like multiple importance sampling, bidirectional path tracing, and Metropolis light transport that greatly improved its efficiency. Using Blinn’s law as a guide, we believe that these significant improvements in efficiency were critical to practical adoption of these approaches.

Around this time, as computers became faster and more parallel, a number of researchers started pursuing real-time ray tracing; Wald, Slusallek, and Benthin wrote an influential paper that described a highly optimized ray tracer that was much more efficient than previous ray tracers (Wald et al. 2001b). Many subsequent papers introduced increasingly more efficient ray-tracing algorithms. Though most of this work wasn’t physically based, the results led to great progress in ray-tracing acceleration structures and performance of the geometric components of ray tracing. Because physically based rendering generally makes substantial use of ray tracing, this work has in turn had the same helpful effect as faster computers have, making it possible to render more complex scenes with physical approaches.

At this point, we’ll end our summary of the key steps in the research progress of physically based rendering; much more has been done. The “Further Reading” sections in all of the subsequent chapters of this book cover this work in detail.

1.7.2 Production

With more capable computers in the 1980s, computer graphics could start to be used for animation and film production. Early examples include Jim Blinn’s rendering of the Voyager 2 Flyby of Saturn in 1981 and visual effects in the movies Star Trek II: The Wrath of Khan (1982), Tron (1982), and The Last Starfighter (1984).

In early production use of computer-generated imagery, rasterization-based rendering (notably, the Reyes algorithm (Cook et al. 1987)) was the only viable option. One reason was that not enough computation was available for complex reflection models or for the global lighting effects that physically based ray tracing could provide. More significantly, rasterization had the important advantage that it didn’t require that the entire scene representation fit into main memory.

When RAM was much less plentiful, almost any interesting scene was too large to fit into main memory. Rasterization-based algorithms made it possible to render scenes while having only a small subset of the full scene representation in memory at any time. Global lighting effects are difficult to achieve if the whole scene can’t fit into main memory; for many years, with limited computer systems, content creators effectively decided that geometric and texture complexity was more important to visual realism than lighting complexity (and in turn physical accuracy).

Many practitioners at this time also believed that physically based approaches were undesirable for production: one of the great things about computer graphics is that one can cheat reality with impunity to achieve a desired artistic effect. For example, lighting designers on regular movies often struggle to place light sources so that they aren’t visible to the camera or spend a lot of effort placing a light to illuminate an actor without shining too much light on the background. Computer graphics offers the opportunity to, for example, implement a light source model that shines twice as much light on a character as on a background object, in a fairly straightforward manner. For many years, this capability seemed much more useful than physical accuracy.

Visual effects practitioners who had the specific need to match rendered imagery to filmed real-world environments pioneered capturing real-world lighting and shading effects and were early adopters of physically based approaches in the late 1990s and early 2000s. (See Snow (2010) for a history of ILM’s early work in this area, for example.)

During this time, Blue Sky Studios adopted a physically based pipeline early in their history (Ohmer 1997). The photorealism of an advertisement they made for a Braun shaver in 1992 caught the attention of many, and their short film, Bunny, shown in 1998, was an early example of Monte Carlo global illumination used in production. Its visual look was substantially different from those of films and shorts rendered with Reyes and was widely noted. Subsequent feature films from Blue Sky also followed this approach. Unfortunately, Blue Sky never published significant technical details of their approach, limiting their wider influence.

During the early 2000s, the mental ray ray-tracing system was used by a number of studios, mostly for visual effects. It was a very efficient ray tracer with sophisticated global illumination algorithm implementations. The main focus of its developers was computer-aided design and product design applications, so it lacked features like the ability to handle extremely complex scenes and the enormous numbers of texture maps that film production demanded.

After Bunny, another watershed moment came in 2001, when Marcos Fajardo came to the SIGGRAPH with an early version of his Arnold renderer. He showed images in the Monte Carlo image synthesis course that not only had complex geometry, textures, and global illumination but also were rendered in tens of minutes. While these scenes weren’t of the complexity of those used in film production at the time, his results showed many the creative opportunities from global illumination in complex scenes.

Fajardo brought Arnold to Sony Pictures Imageworks, where work started to transform it to a production-capable physically based rendering system. Work on efficient motion blur, programmable shading, support for massively complex scenes and deferred loading of scene geometry, and support for texture caching, where only a small subset of the texture in the scene is kept in memory, were all important areas to be addressed. Arnold was first used on the movie Monster House and is now generally available as a product.

In the mid-2000s, Pixar’s RenderMan renderer started to support hybrid rasterization and ray-tracing algorithms and included a number of innovative algorithms for computing global illumination solutions in complex scenes. RenderMan was recently rewritten to be a physically based ray tracer, following the general system architecture of pbrt (Christensen 2015).

One of the main reasons that physically based Monte Carlo approaches to rendering have been successful in production is that they end up improving the productivity of artists. Some of the important factors have been:

  • The algorithms involved have essentially just a single quality knob: how many samples to take per pixel; this is extremely helpful for artists. Ray-tracing algorithms are also suited to both progressive refinement and quickly computing rough previews by taking just a few samples per pixel; rasterization-based renderers don’t have equivalent capabilities.
  • Adopting physically based reflection models has made it easier to design surface materials. Earlier, when reflection models that didn’t necessarily conserve energy were used, an object might be placed in a single lighting environment while its surface reflection parameters were adjusted. The object might look great in that environment, but it would often appear completely wrong when moved to another lighting environment because surfaces were actually reflecting too little or too much energy: surface properties had been set to unreasonable values.
  • The quality of shadows computed with ray tracing is much better than it is with rasterization. Eliminating the need to tweak shadow map resolutions, biases, and other parameters has eliminated an unpleasant task of lighting artists. Further, physically based methods bring with them bounce lighting and other soft-lighting effects from the method itself, rather than as an artistically tuned manual process.

As of this writing, physically based rendering is used widely for producing computer-generated imagery for movies; Figures 1.22 and 1.23 show images from two recent movies that used physically based approaches.

Figure 1.22: Gravity (2013) featured spectacular computer-generated imagery of a realistic space environment with volumetric scattering and large numbers of anisotropic metal surfaces. The image was generated using Arnold, a physically based rendering system that accounts for global illumination. Image courtesy of Warner Bros. and Framestore.

Figure 1.23: This image from The Hobbit: The Battle of the Five Armies (2014) was also rendered using a physically based rendering system; the characters feature heterogeneous subsurface scattering and vast amounts of geometric detail. Image by Weta Digital, courtesy of Warner Bros. and Metro-Goldwyn-Mayer.