Cached k-d tree search for ICP algorithms
| Autor(en): | Nüchter, A. Lingemann, K. Hertzberg, J. |
Stichwörter: | Algorithms; Boolean functions; Bubbles (in fluids); Digital arithmetic; Inductively coupled plasma; Iterative methods; Standards; Three dimensional, Closest points; Data sets; De facto standards; Digital imaging; Geometric alignment; International conferences; Iterative closest point (ICP); Iterative closest point (ICP) algorithm; Iterative closet point (ICP) algorithms; K D trees; Relative pose; Search procedures; Three dimensional (3 D) modeling, Trees (mathematics) | Erscheinungsdatum: | 2007 | Journal: | 3DIM 2007 - Proceedings 6th International Conference on 3-D Digital Imaging and Modeling | Startseite: | 419 | Seitenende: | 426 | Zusammenfassung: | The ICP (Iterative Closest Point) algorithm is the de facto standard for geometric alignment of three-dimensional models when an initial relative pose estimate is available. The basis of ICP is the search for closest points. Since the development of ICP, k-d trees have been used to accelerate the search. This paper presents a novel search procedure, namely cached k-d trees, exploiting iterative behavior of the ICP algorithm. It results in a significant speedup of about 50% as we show in an evaluation using different data sets. © 2007 IEEE. |
Beschreibung: | Conference of 6th International Conference on 3-D Digital Imaging and Modeling, 3DIM 2007 ; Conference Date: 21 August 2007 Through 23 August 2007; Conference Code:72666 |
ISBN: | 9780769529394 | DOI: | 10.1109/3DIM.2007.15 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-47349094672&doi=10.1109%2f3DIM.2007.15&partnerID=40&md5=8600e1f920f36e80e7bf4d85adbba783 |
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