3D object pose detection using foreground/background segmentation
Open accessAbstract
This paper addresses the challenge of detecting and localizing a poorly textured known object, by initially estimating its complete 3D pose in a video sequence. Our solution relies on the 3D model of the object and synthetic views. The full pose estimation process is then based on foreground/background segmentation and on an efficient probabilistic edge-based matching and alignment procedure with the set of synthetic views, classified through an unsupervised learning phase. Our study focuses on space robotics applications and the method has been tested on both synthetic and real images, showing its efficiency and convenience, with reasonable computational costs.
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Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Proenca, P. F., & Gao, Y. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197244 · Cited by 229 · see citing works
“As for URSO, we collected one dataset for the dragon spacecraft and two datasets for the soyuz model with different operating ranges: soyuz_easy with [10-20] m and soyuz_hard with [10-40]”
BackgroundMethodDeep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
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ChiNet: Deep recurrent convolutional learning for multimodal spacecraft pose estimation
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- Authors
- Petit, A., Marchand, E., Sekkal, R., & Kanani, K.
- Affiliations
- Centre Inria de l'Université de Rennes (FR)
- Institut de Recherche en Informatique et Systèmes Aléatoires (FR)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 1858-1865
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1489610562
- Cited by
- 13
- References
- 25
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1
- FWCI
- 1.87
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- 3D object pose estimation, foreground-background segmentation, edge-based matching, 3D object models, unsupervised learning, space robotics, probabilistic alignment
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