CMT-6D: a lightweight iterative 6DoF pose estimation network based on cross-modal Transformer-Reference-Cited by-同舟云学术

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CMT-6D: a lightweight iterative 6DoF pose estimation network based on cross-modal Transformer

Published:2024-06-17 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Liu Suyi,Xu Fang,Wu Chengdong,Chi Jianning,Yu Xiaosheng,Wei Longxing,Leng Chuanjiang

Funder

Foundation of Ministry of Industry and Information Technology

National Natural Science Foundation of China under Grant nos

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00371-024-03520-1.pdf

Reference57 articles.

1. Zhu, M., Derpanis, K.G., Yang, Y., Brahmbhatt, S., Zhang, M., Phillips, C., Lecce, M., Daniilidis, K.: Single image 3d object detection and pose estimation for grasping. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3936–3943 (2014). https://doi.org/10.1109/ICRA.2014.6907430

2. Rangesh, A., Trivedi, M.M.: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Trans. Intel. Veh. 5(3), 449–460 (2020). https://doi.org/10.1109/TIV.2020.2966074

3. Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Visual Comput. Graph. 22(12), 2633–2651 (2016). https://doi.org/10.1109/TVCG.2015.2513408

4. Xu, D., Anguelov, D., Jain, A.: Pointfusion: Deep sensor fusion for 3d bounding box estimation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 244–253 (2018). https://doi.org/10.1109/CVPR.2018.00033

5. Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77–85 (2017). https://doi.org/10.1109/CVPR.2017.16

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