We present a framework for generating, compressing and rendering of Surface Light Field (SLF) data. Our methodis based on radiance data generated using physically based rendering methods. Thus the SLF data is generateddirectly instead of re-sampling digital photographs. Our SLF representation decouples spatial resolution fromgeometric complexity. We achieve this by uniform sampling of spatial dimension of the SLF function. For compression,we use Clustered Principal Component Analysis (CPCA). The SLF matrix is first clustered to low frequencygroups of points across all directions. Then we apply PCA to each cluster. The clustering ensures that the withinclusterfrequency of data is low, allowing for projection using a few principal components. Finally we reconstructthe CPCA encoded data using an efficient rendering algorithm. Our reconstruction technique ensures seamlessreconstruction of discrete SLF data. We applied our rendering method for fast, high quality off-line rendering andreal-time illumination of static scenes. The proposed framework is not limited to complexity of materials or lightsources, enabling us to render high quality images describing the full global illumination in a scene.