Publikationen

Autor	Bormann, Pascal; Krämer, Michel
Datum	2020
Art	Conference Paper
Abstrakt	We introduce a system for fast, scalable indexing of arbitrarily sized point clouds based on a task-parallel computation model. Points are sorted using Morton indices in order to efficiently distribute sets of related points onto multiple concurrent indexing tasks. To achieve a high degree of parallelism, a hybrid top-down, bottom-up processing strategy is used. Our system achieves a 2.3x to 9x speedup over existing point cloud indexing systems while retaining comparable visual quality of the resulting acceleration structures. It is also fully compatible with widely used data formats in the context of web-based point cloud visualization. We demonstrate the effectiveness of our system in two experiments, evaluating scalability and general performance while processing datasets of up to 52.5 billion points.
Konferenz	Italian Chapter Conference "Smart Tools and Applications in Computer Graphics" (STAG) 2020
Url	https://publica.fraunhofer.de/handle/publica/409262

A system for fast and scalable point cloud indexing using task parallelism