Robust Pose Estimation by Fusing Partial Color and Depth Imagery
References
- Feng, B., Liu, Z., Zhang, H., Fan, H. (2024). Research on the measurement system and remote calibration technology of a dual linear array camera. Measurement Science Review, 24 (3), 105–112.
https://doi.org/10.2478/msr-2024-0015 - Murphy-Chutorian, E., Trivedi, M. M. (2010). Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness. IEEE Transactions on Intelligent Transportation Systems, 11 (2), 300–311.
https://doi.org/10.1109/TITS.2010.2044241 - Yang, T., Zhao, Q., Wang, X., Zhou, Q. (2018). Sub-pixel chessboard corner localization for camera calibration and pose estimation. Applied Sciences, 8 (11), 2118.
https://doi.org/10.3390/app8112118 - Zhao, R., Ali, H., van der Smagt, P. (2017). Two-stream RNN/CNN for action recognition in 3D videos. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE.
https://doi.org/10.1109/IROS.2017.8206288 - Andriluka, M., Roth, S., Schiele, B. (2010). Monocular 3D pose estimation and tracking by detection. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE.
https://doi.org/10.1109/CVPR.2010.5540156 - Kalaitzakis, M., Cain, B., Carroll, S., Ambrosi, A., Whitehead, C., Vitzilaios, N. (2021). Fiducial markers for pose estimation. Journal of Intelligent & Robotic Systems, 101, 71.
https://doi.org/10.1007/s10846-020-01307-9 - Besl, P. J., McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (2), 239–256.
https://doi.org/10.1109/34.121791 - Myronenko, A., Song, X. (2010). Point set registration: Coherent point drifts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (12), 2262–2275.
https://doi.org/10.1109/TPAMI.2010.46 - Delavari, M., Foruzan, A. H., Chen, Y.-W. (2019). Accurate point correspondences using a modified coherent point drift algorithm. Biomedical Signal Processing and Control, 52, 429–444.
https://doi.org/10.1016/j.bspc.2017.02.009 - Biber, P., Strasser, W. (2003). The normal distributions transform: A new approach to laser scan matching. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003). IEEE, 3, 2743–2748.
https://doi.org/10.1109/IROS.2003.1249285 - Opromolla, R., Fasano, G., Rufino, G., Grassi, M. (2015). A model-based 3D template matching technique for pose acquisition of an uncooperative space object. Sensors, 16 (3), 6360–6382.
https://doi.org/10.3390/s150306360 - Picos, K., Diaz-Ramirez, V. H., Kober, V., Montemayor, A. S., Pantrigo, J. J. (2016). Accurate three-dimensional pose recognition from monocular images using template matched filtering. Optical Engineering, 55 (6), 063102.
https://doi.org/10.1117/1.OE.55.6.063102 - Chen, S., Liang, L., Liang, W., Foroosh, H. (2016). 3D pose tracking with multitemplate warping and SIFT correspondences. IEEE Transactions on Circuits and Systems for Video Technology, 26 (11), 2043–2055.
https://doi.org/10.1109/TCSVT.2015.2452782 - Leng, D. W., Sun, W. D. (2011). Contour-based iterative pose estimation of 3D rigid object. IET Computer Vision, 5 (5), 291–300.
https://doi.org/10.1049/iet-cvi.2010.0098 - Schlobohm, J., Pösch, A., Reithmeier, E., Rosenhahn, B. (2016). Improving contour based pose estimation for fast 3D measurement of free form objects. Measurement, 92, 79–82.
https://doi.org/10.1016/j.measurement.2016.05.093 - Zhang, X., Jiang, Z., Zhang, H., Wei, Q. (2018). Vision-based pose estimation for textureless space objects by contour points matching. IEEE Transactions on Aerospace and Electronic Systems, 54 (5), 2342–2355.
https://doi.org/10.1109/TAES.2018.2815879 - Wang, B., Zhong, F., Qin, X. (2019). Robust edge-based 3D object tracking with direction-based pose validation. Multimedia Tools and Applications, 78 (9), 12307–12331.
https://doi.org/10.1007/s11042-018-6727-5 - He, Z., Jiang, Z., Zhao, X., Zhang, S., Wu, C. (2020). Sparse template-based 6-D pose estimation of metal parts using a monocular camera. IEEE Transactions on Industrial Electronics, 67 (1), 390–401.
https://doi.org/10.1109/TIE.2019.2897539 - Tsai, C.-Y., Hsu, K.-J., Nisar, H. (2018). Efficient model-based object pose estimation based on multi-template tracking and PnP algorithms. Algorithms, 11 (8), 122.
https://doi.org/10.3390/a11080122 - Song, K.-T., Wu, C.-H., Jiang, S.-Y. (2017). CAD-based pose estimation design for random bin picking using a RGB-D camera. Journal of Intelligent & Robotic Systems, 87, 455–470.
https://doi.org/10.1007/s10846-017-0501-1 - Zeng, A., Yu, K.-T., Song, S., Suo, D., Walker, E., Rodriguez, A. (2017). Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge. In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE.
https://doi.org/10.1109/ICRA.2017.7989165 - Le, T., Hamilton, L., Torralba, A. (2017). Benchmarking convolutional neural networks for object segmentation and pose estimation. In 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE.
https://doi.org/10.1109/AIPR.2017.8457943 - Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C. (2014). Learning 6D object pose estimation using 3D object coordinates. In Computer Vision - ECCV 2014. Springer, LNIP 8690, 536–551.
https://doi.org/10.1007/978-3-319-10605-2_35 - Su, Y., Rambach, J., Pagani, A., Stricker, D. (2021). SynPo-Net—Accurate and fast CNN-based 6DoF object pose estimation using synthetic training. Sensors, 21 (1), 300.
https://doi.org/10.3390/s21010300 - Deng, J., Pan, Y., Yao, T., Zhou, W., Li, H., Mei, T. (2020). Single shot video object detector. IEEE Transactions on Multimedia, 23, 846–858.
https://doi.org/10.1109/TMM.2020.2990070 - Dosovitskiy, A., Fischer, P., Ilg, E., Häusser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T. (2015). FlowNet: Learning optical flow with convolutional networks. In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2758–2766.
https://doi.org/10.1109/ICCV.2015.316
DOI: https://doi.org/10.2478/msr-2025-0033 | Journal eISSN: 1335-8871
Language: English
Page range: 309 - 314
Submitted on: Sep 29, 2024
Accepted on: Sep 16, 2025
Published on: Oct 31, 2025
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Related subjects:
© 2025 Mehmet Akif Alper, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.