The DOI matches its DataCite record; 56 of the 60 references it cites were found in a registry or an index.
Spacecraft Pose Estimation Dataset (SPEED)
Open accessAbstract
This repository contains Spacecraft PosE Estimation Dataset (SPEED), which is used to train and evaluate the performance of deep learning models for pose estimation of noncooperative spacecraft. SPEED consists of synthetic as well as actual camera images of a mock-up of the Tango spacecraft from the PRISMA mission. The synthetic images are created by fusing OpenGL-based renderings of the spacecraft’s 3D model with actual images of the Earth captured by the Himawari-8 meteorological satellite. The actual camera images are created using a 7 degrees-of-freedom robotic arm, which positions and orients a vision-based sensor with respect to a full-scale mock-up of the Tango spacecraft. Custom illumination devices simulate the Earth albedo and Sun light with high fidelity to emulate the illumination conditions present in space. SPEED was used in the international competition for spacecraft pose estimation co-hosted by the Advanced Concepts Team of the European Space Agency and the Space Rendezvous Laboratory of Stanford University.
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“Existing datasets, such as Spacecraft PosE Estimation Dataset (SPEED), have so far mostly relied on synthetic images for both training and validation, which are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images.”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [16]
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“To the best of the authors' knowledge, the only publicly available benchmark dataset on spacecraft pose estimation with fully labeled images from multiple sources is the Spacecraft PosE Estimation Dataset (SPEED) [16] which consists of 15,300 images of the Tango spacecraft from the PRISMA mission [17, 18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [16]
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In the citing paper
“To the best of the authors' knowledge, the only publicly available benchmark dataset on spacecraft pose estimation with fully labeled images from multiple sources is the Spacecraft PosE Estimation Dataset (SPEED) [16] which consists of 15,300 images of the Tango spacecraft from the PRISMA mission [17, 18].”
From SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap (2022) · cites this paper as [16]
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Research works printed but not linked: 5
- [9][9] S. Sharma, 'Pose estimation of uncooperative spacecraft using monocular vision and deep learning,' Ph.D. dissertation, Stanford University, Department of Aeronautics & Astronautics, Aug 2019. -
- [10][10] C. Beierle, 'High fidelity validation of vision-based sensors and algorithms for spaceborne navigation,' Ph.D. dissertation, Stanford University, Department of Aeronautics & Astronautics, Mar 2019. -
- [24][24] S. Sharma and S. D'Amico, 'Reduced-dynamics pose estimation for non-cooperative spacecraft rendezvous using monocular vision,' in 38th AAS Guidance and Control Conference, Breckenridge, Colorado , 2017. -
- [26][26] S. D'Amico, P. Bodin, M. Delpech, and R. Noteborn, 'PRISMA,' in Distributed Space Missions for Earth System Monitoring Space Technology Library , M. D'Errico, Ed., 2013, vol. 31, ch. 21, pp. 599-637. -
- [49][49] T. H. Park, S. Sharma, and S. D'Amico, 'Towards robust learningbased pose estimation of noncooperative spacecraft,' in 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine , August 11-15 2019. -
Cited by
Sorted by citations
Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous
Sharma, S., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2999148 · Cited by 196
Self-citationArtificial intelligence for satellite communication: A review
Fourati, F., & Alouini, M. (2021) · Intelligent and Converged Networks · doi:10.23919/icn.2021.0015 · Cited by 197
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap
Park, T. H., Martens, M., Lecuyer, G., Izzo, D., & D'Amico, S. (2022) · 2022 IEEE Aerospace Conference (AERO) · doi:10.1109/aero53065.2022.9843439 · Cited by 170
Self-citationWide-Depth-Range 6D Object Pose Estimation in Space
Hu, Y., Speierer, S., Jakob, W., Fua, P., & Salzmann, M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01561 · Cited by 134
Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap
Park, T. H., & D’Amico, S. (2024) · Advances in Space Research · doi:10.1016/j.asr.2023.03.036 · Cited by 125
Self-citation
Metadata
- Authors
- Sharma, S., Park, T. H., & D'Amico, S.
- Affiliations
- Stanford University (US)
- Venue
- Stanford Digital Repository
- Publisher
- Stanford Digital Repository
- Published
- 2026
- Volume
- 56
- Issue
- 5
- Pages
- 4083-4098
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2988274083
- Cited by
- 304
- References
- 60
- Citations by year
- 2026: 51
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Stanford Digital Repository
- Funders
- Air Force Office of Scientific Research
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Social Sciences › Political Science and International Relations › Space exploration and regulation
- Keywords
- satellite pose estimation, monocular vision, on-orbit servicing, space debris removal, synthetic images, machine learning competition, performance indicators, testbed
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