Inferring Occluded Geometry Improves Performance When Retrieving an Object from Dense Clutter-Reference-Cited by-同舟云学术

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Inferring Occluded Geometry Improves Performance When Retrieving an Object from Dense Clutter

Published:2022 Issue: Volume: Page:376-392
ISSN:2511-1256
Container-title:Springer Proceedings in Advanced Robotics
language:
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Author:

Price Andrew,Jin Linyi,Berenson Dmitry

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95459-8_23

Reference34 articles.

1. Jonschkowski, R., Eppner, C., Höfer, S., Martín-Martín, R., Brock, O.: Probabilistic multiclass segmentation for the Amazon Picking Challenge. In: IROS, pp. 1–7. IEEE (2016)

2. Schwarz, M., et al.: NimbRo picking: versatile part handling for warehouse automation. In: ICRA, pp. 3032–3039. IEEE (2017)

3. Milan, A., et al.: Semantic segmentation from limited training data. In: ICRA, pp. 1908–1915. IEEE (2018)

4. Varley, J., DeChant, C., Richardson, A., Ruales, J., Allen, P.: Shape completion enabled robotic grasping. In: IROS, pp. 2442–2447. IEEE (2017)

5. Yang, B., Wen, H., Wang, S., Clark, R., Markham, A., Trigoni, N.: 3D object reconstruction from a single depth view with adversarial learning. In: ICCV Workshop (2017)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Resolution Complete In-Place Object Retrieval given Known Object Models;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

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