SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Open accessAbstract
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.
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- Authors
- Kehl, W., Manhardt, F., Tombari, F., Ilic, S., & Navab, N.
- Affiliations
- Toyota Research Institute (US)
- Technical University of Munich (DE)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1530-1538
- OpenAlex
- W2768840867
- Cited by
- 1,047
- References
- 28
- Citations by year
- 2026: 29 · 2025: 84 · 2024: 146 · 2023: 140 · 2022: 148 · 2021: 175 · 2020: 157 · 2019: 129 · 2018: 38 · 2017: 1
- FWCI
- 25.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Toyota Motor Corporation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- 3D object detection, 6D pose estimation, single-shot detection, synthetic training data, real-time pose estimation
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