Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
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In robotic vision tasks, training data from simulators always have different textures, lighting, and camera positions from realistic environments.
“In robotic vision tasks including object localization [53], object detection [54], pose estimation [55], and semantic segmentation [56], the training data from simulator always have different textures, lighting, and camera positions from the realistic environments.”
From Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey (2020) · cites this paper as [55]
Cites
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124 · cited 7× · see citing works
“[36] demonstrated the potential of the Domain Randomization (DR) paradigm for 3D shape detection using CNNs.”
BackgroundMethodInfluentialUnsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603 · see citing works
3D Pose Regression Using Convolutional Neural Networks
Mahendran, S., Ali, H., & Vidal, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.73 · Cited by 210 · see citing works
Going Further with Point Pair Features
Hinterstoisser, S., Lepetit, V., Rajkumar, N., & Konolige, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_51 · Cited by 201 · see citing works
How Useful Is Photo-Realistic Rendering for Visual Learning?
Movshovitz-Attias, Y., Kanade, T., & Sheikh, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49409-8_18 · Cited by 118 · see citing works
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Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Zhao, W., Queralta, J. P., & Westerlund, T. (2020) · 2020 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308468 · Cited by 941 · see citing works
Spacecraft pose estimation dataset (speed)
Sharma, S., Park, T. H., & D'Amico, S. (2026) · Stanford Digital Repository · doi:10.25740/dz692fn7184 · Cited by 304
Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results
Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Martens, M., & D'Amico, S. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2989063 · Cited by 304 · see citing works
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00343 · Cited by 203 · see citing works
Where2Act: From Pixels to Actions for Articulated 3D Objects
Mo, K., Guibas, L., Mukadam, M., Gupta, A., & Tulsiani, S. (2021) · arXiv · doi:10.48550/arxiv.2101.02692 · Cited by 132 · see citing works
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Facil, J. M., Ummenhofer, B., Zhou, H., Montesano, L., Brox, T., & Civera, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01210 · Cited by 130 · see citing works
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
Wang, G., Manhardt, F., Liu, X., Ji, X., & Tombari, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3136301 · Cited by 69 · see citing works
Huo, Y., Li, Z., & Zhang, F. (2020) · IEEE Access · doi:10.1109/access.2020.3041415 · Cited by 44 · see citing works
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
Ulmer, M., Durner, M., Sundermeyer, M., Stoiber, M., & Triebel, R. (2023) · 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341511 · Cited by 23 · see citing works
Self-citation
- Authors
- Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., & Triebel, R.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Corresponding author
- Sundermeyer, Martin
- Durner, Maximilian
- Brucker, Manuel
- Triebel, Rudolph
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 712-729
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2895439318
- Cited by
- 564
- References
- 6
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 56 · 2023: 69 · 2022: 98 · 2021: 107 · 2020: 106 · 2019: 74 · 2018: 6
- FWCI
- 22.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- elib (German Aerospace Center) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 6D pose estimation, 3D orientation estimation, denoising autoencoder, domain randomization, synthetic training data, latent space, real-time pose estimation, LINEMOD
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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