Our goal in this article is to propose a fast and easy to implement BRDF modeling method that provides both accurate and compact representations for all types of BRDF, i.e., isotropic or anisotropic. To achieve this objective, we use a Bayesian regression method with a Gaussian process prior which allows obtaining compact BRDF representations in a purely analytical way. For this purpose, we use a generalzed distance covariance kernel which is much better suited to BRDF features than the usual Gaussian kernel. To speed up the processing, we adapt this method to the specificities of BRDFs through an appropriate input data structure and distribution of observations so as to drastically reduce the problem dimensionality through an efficient factorization method. In this way, all calculations at both fitting and rendering steps are reduced to basic matrix products and the computation of a BRDF representation with our modeling method takes only a few seconds. Furthermore, rather than using a systematic approach as in state-of-the-art methods, the size and complexity of the BRDF representation can be adapted to the application requirements as regards the fitting accuracy and rendering constraints. Besides, our BRDF representation can be easily converted to spherical harmonics expansions, which allows easier integration in usual rendering algorithms. We also propose importance sampling methods derived from our BRDF modeling method that leads to fast and easy implementations. Experimental applications of our method to various types of isotropic and anisotropic BRDFs show that state-of-the-art methods can be outperformed in most cases by using a small set of observations for the regression.

Physically-based synthesis of high quality imagery, including global illumination light transport phenomena, results in a significant workload, which makes interactive rendering a very challenging task. We propose a VPL-based ray tracing approach that runs entirely in the GPU and achieves interactive frame rates while handling global illumination light transport phenomena. This approach is based on clustering both shading points and VPLs and computing visibility only among clusters’ representatives. A new massively parallel K-means clustering algorithm, enables efficient execution in the GPU. Rendering artifacts, that could result from the piecewise constant approximation of the VPLs/shading points visibility function introduced by the clustering, are smoothed away by resorting to an innovative approach based on fuzzy clustering and weighted interpolation of the visibility function. The effectiveness of the proposed approach is experimentally verified for a collection of scenes, with frame rates larger than 3 fps and up to 25 fps being demonstrated.