A radiosity method for non-diffuse environments

Abstract

A general radiosity method accounting for all interreflections of light between diffuse and nondiffuse surfaces in complex environments is introduced. As contrasted with previous radiosity methods, surfaces are no longer required to be perfectly diffuse reflectors and emitters. A complete, viewer independent description of the light leaving each surface in each direction is computed, allowing dynamic sequences of images to be rendered with little additional computation per image. Phenomena such as "reflection tracking", reflections following a moving observer across a specular surface are produced. Secondary light sources, such as the light from a spotlight reflecting off a mirror onto a wall are also accounted for.

State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics

“…Such restrictions allowed radiosity method to be view-independent meaning that one computation can be reused for rendering images from arbitrary viewpoints. Creating more complex environments has remained a challenging task and this method has been constantly improving adding support for more complex reflections [26], as well as hierarchical methods to address complex geometries [27] [28] and instant radiosity [29]. However, these advancements made the radiosity algorithm much more computationally expensive, while not following real light behaviour, neither providing physically accurate and photorealistic appearance.…”

A formalization and classification of global illumination methods

“…This value is obtained by the evaluation of Equation (6) for a given set of Markov Chains or paths. We may use importance sampling, such that the probability density function Q (at pixel i) is defined by using the following p and P functions: P(r) = Wi(r) (14) P(r, s) = K(r, s)…”

Fast Global Illumination Including Specular Effects

“…From the earliest such work [12] to the most recent [26] using clustering and wavelets, the storage required for directional discretisation makes these methods unusable for complex scenes which contain highly directional glossy effects such as caustics. Continuous representations (e.g., [22]) also require large numbers of base coefficients, leading to the same problem.…”