Exploiting Local Orientation Similarity for Efficient Ray Traversal of Hair and Fur

Abstract

Hair and fur typically consist of a large number of thin, curved, and densely packed strands which are difficult to ray trace efficiently. A tight fitting spatial data structure, such as a bounding volume hierarchy (BVH), is needed to quickly determine which hair a ray hits. However, the large number of hairs can yield a BVH with a large memory footprint (particularly when hairs are pre-tessellated), and curved or diagonal hairs cannot be tightly bounded within axis aligned bounding boxes. In this paper, we describe an approach to ray tracing hair and fur with improved efficiency, by combining parametrically defined hairs with a BVH that uses both axis-aligned and oriented bounding boxes. This BVH exploits similarity in the orientation of neighboring hairs to increase ray culling efficiency compared to purely axis-aligned BVHs. Our approach achieves about 2× the performance of ray tracing pre-tessellated hair models, while requiring significantly less memory.