Scalable Real-Time Shadows using Clustering and Metric Trees

Abstract

Real-time shadow algorithms based on geometry generally produce high quality shadows. Recent works have considerably improved their efficiency. However, scalability remains an issue because these methods strongly depend on the geometric complexity. This paper focuses on this problem. We present a new real-time shadow algorithm for non-deformable models that scales the geometric complexity. Our method groups triangles into clusters by precomputing bounding spheres or bounding capsules (line-swept spheres). At each frame, we build a ternary metric tree to partition the spheres and capsules according to their apparent distance from the light. Then, this tree is used as an acceleration data structure to determine the visibility of the light for each image point. While clustering allows to scale down the geometric complexity, metric trees allow to encode the bounding volumes of the clusters in a hierarchical data structure. Our experiments show that our approach remains efficient, including with models with over 70 million triangles.