We describe a physically-based Monte Carlo technique for approximating bidirectional reflectance distribution functions (BRDFs) for a large class of geometries by directly simulating optical scattering. The technique is more general than previous analytical models: it removes most restrictions on surface microgeometry. Three main points are described: a new representation of the BRDF, a Monte Carlo technique to estimate the coefficients of the representation, and the means of creating a milliscale BRDF from microscale scattering events. These allow the prediction of scattering from essentially arbitrary roughness geometries. The BRDF is concisely represented by a matrix of spherical harmonic coefficients; the matrix is directly estimated from a geometric optics simulation, enforcing exact reciprocity. The method applies to roughness scales that are large with respect to the wavelength of light and small with respect to the spatial density at which the BRDF is sampled across the surface; examples include brushed metal and textiles. The method is validated by comparing with an existing scattering model and sample images are generated with a physically-based global illumination algorithm.

We present an approach for accelerating hierarchical radiosity by clustering objects. Previous approaches constructed effective hierarchies by subdividing surfaces, but could not exploit a hierarchical grouping on existing surfaces. This limitation resulted in an excessive number of initial links in complex environments. Initial linking is potentially the most expensive portion of hierarchical radiosity algorithms, and constrains the complexity of the environments that can be simulated. The clustering algorithm presented here operates by estimating energy transfers between collections of objects while maintaining reliable error bounds on each transfer. Two methods of bounding the transfers are employed with different tradeoffs between accuracy and time. In contrast with the O(s 2 ) time and space complexity of the initial linking in previous hierarchical radiosity algorithms, the new methods have complexities of O(s log s) and O(s) for both time and space. Using these methods we have obtained speedups of two orders of magnitude for environments of moderate complexity while maintaining comparable accuracy.

In this paper we identify sources of error in global illumination algorithms and derive bounds for each distinct category. Errors arise from three sources: inaccuracies in the boundary data, discretization, and computation. Boundary data consist of surface geometry, reflectance functions, and emission functions, all of which may be perturbed by errors in measurement or simulation, or by simplifications made for computational efficiency. Discretization error is introduced by replacing the continuous radiative transfer equation with a finite-dimensional linear system, usually by means of boundary elements and a corresponding projection method. Finally, computational errors perturb the finite-dimensional linear system through imprecise form factors, inner products, visibility, etc., as well as by halting iterative solvers after a finite number of steps. Using the error taxonomy introduced in the paper we examine existing global illumination algorithms and suggest new avenues of research.

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