Model-based multiple rigid object detection and registration in unstructured range data
We present a two-stage approach to the simultaneous detection and registration of multiple instances of industrial 3D objects in unstructured noisy range data. The first non-local processing stage takes all data into account and computes in parallel multiple localizations of the object along with ro...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
International journal of computer vision
Year: 2011, Jahrgang: 92, Pages: 32-52 |
| ISSN: | 1573-1405 |
| DOI: | 10.1007/s11263-010-0401-3 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s11263-010-0401-3
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| Verfasserangaben: | Dirk Breitenreicher, Christoph Schnörr |
MARC
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| 245 | 1 | 0 | |a Model-based multiple rigid object detection and registration in unstructured range data |c Dirk Breitenreicher, Christoph Schnörr |
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| 520 | |a We present a two-stage approach to the simultaneous detection and registration of multiple instances of industrial 3D objects in unstructured noisy range data. The first non-local processing stage takes all data into account and computes in parallel multiple localizations of the object along with rough pose estimates. The second stage computes accurate registrations for all detected object instances individually by using local optimization. | ||
| 650 | 4 | |a Geometric optimization | |
| 650 | 4 | |a Multiple object detection | |
| 650 | 4 | |a Point set registration | |
| 650 | 4 | |a Range data | |
| 650 | 4 | |a Sparse convex programming | |
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