Resumé
| Originalsprog | Engelsk |
|---|---|
| Titel | Computer Vision - ECCV 2018 : 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part III |
| Redaktører | Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, Yair Weiss |
| Vol/bind | 11207 |
| Forlag | Springer |
| Publikationsdato | sep. 2018 |
| Sider | 19-35 |
| ISBN (Trykt) | 978-3-030-01218-2 |
| ISBN (Elektronisk) | 978-3-030-01219-9 |
| DOI | |
| Status | Udgivet - sep. 2018 |
| Begivenhed | 15th European Conference on Computer Vision - München, Tyskland Varighed: 8. sep. 2018 → 14. sep. 2018 |
Konference
| Konference | 15th European Conference on Computer Vision |
|---|---|
| Land | Tyskland |
| By | München |
| Periode | 08/09/2018 → 14/09/2018 |
| Navn | Lecture Notes in Computer Science |
|---|---|
| ISSN | 0302-9743 |
Fingeraftryk
Citer dette
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BOP: Benchmark for 6D Object Pose Estimation. / Hodan, Tomas; Michel, Frank; Brachmann, Eric; Kehl, Wadim; Buch, Anders Glent; Kraft, Dirk; Drost, Bertram; Vidal, Joel; Ihrke, Stephan; Zabulis, Xenophon; Sahin, Caner; Manhardt, Fabian; Tombari, Federico; Kim, Tae-Kyun; Matas, Jiri; Rother, Carsten.
Computer Vision - ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part III. red. / Vittorio Ferrari; Martial Hebert; Cristian Sminchisescu; Yair Weiss. Bind 11207 Springer, 2018. s. 19-35 (Lecture Notes in Computer Science).Publikation: Bidrag til bog/antologi/rapport/konference-proceeding › Konferencebidrag i proceedings › Forskning › peer review
TY - GEN
T1 - BOP: Benchmark for 6D Object Pose Estimation
AU - Hodan, Tomas
AU - Michel, Frank
AU - Brachmann, Eric
AU - Kehl, Wadim
AU - Buch, Anders Glent
AU - Kraft, Dirk
AU - Drost, Bertram
AU - Vidal, Joel
AU - Ihrke, Stephan
AU - Zabulis, Xenophon
AU - Sahin, Caner
AU - Manhardt, Fabian
AU - Tombari, Federico
AU - Kim, Tae-Kyun
AU - Matas, Jiri
AU - Rother, Carsten
PY - 2018/9
Y1 - 2018/9
N2 - We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.
AB - We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.
U2 - 10.1007/978-3-030-01249-6_2
DO - 10.1007/978-3-030-01249-6_2
M3 - Article in proceedings
SN - 978-3-030-01218-2
VL - 11207
SP - 19
EP - 35
BT - Computer Vision - ECCV 2018
A2 - Ferrari, Vittorio
A2 - Hebert, Martial
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
PB - Springer
ER -