We present a framework for generating, compressing and rendering of SurfaceLight Field (SLF) data. Our method is based on radiance data generated usingphysically based rendering methods. Thus the SLF data is generated directlyinstead of re-sampling digital photographs. Our SLF representation decouplesspatial resolution from geometric complexity. We achieve this by uniform samplingof spatial dimension of the SLF function. For compression, we use ClusteredPrincipal Component Analysis (CPCA). The SLF matrix is first clustered to lowfrequency groups of points across all directions. Then we apply PCA to eachcluster. The clustering ensures that the within-cluster frequency of data is low,allowing for projection using a few principal components. Finally we reconstructthe CPCA encoded data using an efficient rendering algorithm. Our reconstructiontechnique ensures seamless reconstruction of discrete SLF data. We applied ourrendering method for fast, high quality off-line rendering and real-time illuminationof static scenes. The proposed framework is not limited to complexity of materialsor light sources, enabling us to render high quality images describing the full globalillumination in a scene.