Bormann, Pascal ; Krämer, Michel (2020)
A System for Fast and Scalable Point Cloud Indexing Using
Task Parallelism.
Smart Tools and Applications in computer Graphics - Eurographics Italian Chapter Conference 2020 (STAG). virtual Conference (12.11.2020-13.11.2020)
doi: 10.2312/stag.20201250
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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 indexingtasks. To achieve a high degree of parallelism, a hybrid top-down, bottom-up processing strategy is used. Our system achievesa 2.3x to 9x speedup over existing point cloud indexing systems while retaining comparable visual quality of the resultingacceleration 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 performancewhile processing datasets of up to 52.5 billion points.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Bormann, Pascal ; Krämer, Michel |
| Art des Eintrags: | Bibliographie |
| Titel: | A System for Fast and Scalable Point Cloud Indexing Using Task Parallelism |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | The Eurographics Association |
| Veranstaltungstitel: | Smart Tools and Applications in computer Graphics - Eurographics Italian Chapter Conference 2020 (STAG) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 12.11.2020-13.11.2020 |
| DOI: | 10.2312/stag.20201250 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | 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 indexingtasks. To achieve a high degree of parallelism, a hybrid top-down, bottom-up processing strategy is used. Our system achievesa 2.3x to 9x speedup over existing point cloud indexing systems while retaining comparable visual quality of the resultingacceleration 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 performancewhile processing datasets of up to 52.5 billion points. |
| Freie Schlagworte: | Point clouds, Acceleration structures, Parallel algorithms, Spatial data |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 02 Dez 2020 12:10 |
| Letzte Änderung: | 02 Dez 2020 12:10 |
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