Learning Based Compression of Surface Light Fields for Real-time Rendering of Global Illumination Scenes

摘要

We present an algorithm for compression and real-time rendering of surface light fields (SLF) encoding the visual appearance of objects in static scenes with high frequency variations. We apply a non-local clustering in order to exploit spatial coherence in the SLFdata. To efficiently encode the data in each cluster, we introducea learning based approach, Clustered Exemplar Orthogonal Bases(CEOB), which trains a compact dictionary of orthogonal basispairs, enabling efficient sparse projection of the SLF data. In ad-dition, we discuss the application of the traditional Clustered Principal Component Analysis (CPCA) on SLF data, and show that inmost cases, CEOB outperforms CPCA, K-SVD and spherical harmonics in terms of memory footprint, rendering performance andreconstruction quality. Our method enables efficient reconstructionand real-time rendering of scenes with complex materials and lightsources, not possible to render in real-time using previous methods.