Abstract: 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.