Data structures and algorithms for real-time ray tracing at the University of Texas at Austin
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Modern rendering systems require fast and efficient acceleration structures in order to compute visibility in real time. I present several novel data structures and algorithms for computing visibility with high performance. In particular, I present two algorithms for improving heuristic based acceleration structure build. These algorithms, when used in a demand driven way, have been shown to improve build performance by up to two orders of magnitude. Additionally, I introduce ray tracing in perspective transformed space. I demonstrate that ray tracing in this space can significantly improve visibility performance for near-common origin rays such as eye and shadow rays. I use these data structures and algorithms to support a key hypothesis of this dissertation: “There is no silver bullet for solving the visibility problem; many different acceleration structures will be required to achieve the highest performance.” Specialized acceleration structures provide significantly better performance than generic ones and building many specialized structures requires high performance build techniques. Additionally, I present an optimization-based taxonomy for classifying acceleration structures and algorithms in order to identify which optimizations provide the largest improvement in performance. This taxonomy also provides context for the algorithms I present. Finally, I present several novel cost metrics (and a correction to an existing cost metric) to improve visibility performance when using metric based acceleration structures.