6D object pose estimation based on dense convolutional object center voting with improved accuracy and efficiency-Reference-Cited by-同舟云学术

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6D object pose estimation based on dense convolutional object center voting with improved accuracy and efficiency

Published:2023-11-25 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Ullah Faheem,Wei Wu,Fan Zhun,Yu Qiuda

Funder

Science and Technology Planning Project of Guangdong Province, China

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-023-03113-4.pdf

Reference49 articles.

1. Tan, M., Pang, R., Le, Q. V.: EfficientDet : Scalable and Efficient Object Detection, in IEEE/CVF conference on computer vision and pattern recognition, pp. 10781–10790 (2020)

2. Lepetit V.: BB8 : a scalable, accurate, robust to partial occlusion method for predicting. Proc. IEEE Int. Conf. Comput. Vis., pp. 3828–3836, (2017)

3. Tekin, B., Sinha, S. N., & Fua, P.: Real-time seamless single shot 6d object pose prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 292-301). (2018). https://doi.org/10.1109/CVPR.2018.00038.

4. Xiang Y., Schmidt T., Narayanan V., and Fox D.: PoseCNN : a convolutional neural network for 6D object pose estimation in cluttered scenes. (2018)

5. Capellen C., Schwarz M., and Behnke S.: ConvPoseCNN: Dense convolutional 6D object pose estimation. VISIGRAPP 2020 - Proc. 15th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., 5, 162–172, (2020). https://doi.org/10.5220/0008990901620172.

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