2013/07/19 by Yan Gu, Yong He, Kayvon Fatahalian +1 · 78 citations
Computer Science · #Data Management and Algorithms #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Cluster analysis #Computer science #Parallelizable manifold #Hierarchical clustering #Hierarchical clustering of networks #Bounding overwatch #Set (abstract data type) #Single-linkage clustering #Canopy clustering algorithm #Correlation clustering #Algorithm #Data mining #Artificial intelligence
paper · doi:10.1145/2492045.2492054
openalex publication_date 2013/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We introduce Approximate Agglomerative Clustering (AAC), an efficient, easily parallelizable algorithm for generating high-quality bounding volume hierarchies using agglomerative clustering. The main idea of AAC is to compute an approximation to the true greedy agglomerative clustering solution by restricting the set of candidates inspected when identifying neighboring geometry in the scene. The result is a simple algorithm that often produces higher quality hierarchies (in terms of subsequent ray tracing cost) than a full sweep SAH build yet executes in less time than the widely used top-down, approximate SAH build algorithm based on binning.