Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes
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The reprojection error and average distance error have the disadvantage that orientation and position sensitivity is dependent on the choice of keypoints.
“Both have the disadvantage that the orientation and position sensitivity is dependent on the choice of keypoints, since the slope of orientation error is proportional to the distance of the keypoints from the origin of the target's body frame.”
From Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results (2020) · cites this paper as [29]
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078 · see citing works
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SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works
Unsupervised 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
Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image
Brachmann, E., Michel, F., Krull, A., Yang, M. Y., Gumhold, S., & Rother, C. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.366 · Cited by 530 · see citing works
Segmentation-Driven 6D Object Pose Estimation
Hu, Y., Hugonot, J., Fua, P., & Salzmann, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00350 · Cited by 333 · 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
“phical Processing Units (GPU) and the availability of large-scale datasets to train the DNN, such as ImageNet for classification [5], MS COCO for object detection [28], and LINEMOD for pose estimation [29] of ordinary household objects. While various DNN-based approaches have been proposed to perform pose estimation [3…”
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
Self-citationA dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place
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Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv · doi:10.48550/arxiv.2107.04649 · Cited by 74 · see citing works
Wang, Z., Chen, M., Guo, Y., Li, Z., & Yu, Q. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3250385 · Cited by 64 · 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
Machine Vision based Sample-Tube Localization for Mars Sample Return
Daftry, S., Ridge, B., Seto, W., Pham, T.-H., Ilhardt, P., Maggiolino, G., van der Merwe, M., Brinkman, A., Mayo, J., Kulczyski, E., et al. (2021) · 2021 IEEE Aerospace Conference (50100) · doi:10.1109/aero50100.2021.9438364 · Cited by 10 · see citing works
- Authors
- Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N.
- Affiliations
- Technical University of Munich (DE)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 548-562
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1526868886
- Cited by
- 1,161
- References
- 2
- Citations by year
- 2026: 58 · 2025: 93 · 2024: 127 · 2023: 131 · 2022: 115 · 2021: 153 · 2020: 150 · 2019: 111 · 2018: 62 · 2017: 56 · 2016: 51 · 2015: 31 · 2014: 16 · 2013: 7
- FWCI
- 47.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 3D object detection, pose estimation, textureless objects, cluttered scenes, Kinect sensor, LINEMOD, template-based detection, 3D model, real-time tracking, object manipulation, robot vision, benchmark dataset
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