Center-push loss for joint view-based 3D model classification and retrieval feature learning-Reference-Cited by-同舟云学术

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Center-push loss for joint view-based 3D model classification and retrieval feature learning

Published:2021-05-22 Issue: Volume: Page:
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Wang Dong,Wang Bin,Yao Hongxun,Liu Hong

Funder

National Natural Science Foundation of China

Self-Planned Task of State Key Laboratory of Robotics and System

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-021-01923-4.pdf

Reference30 articles.

1. Bai, S., Bai, X., Zhou, Z., Zhang, Z., Tian, Q., Latecki, L.J.: GIFT: towards scalable 3D shape retrieval. IEEE Trans. Multimed. 19(6), 1257–1271 (2017)

2. Chen, S., Zheng, L., Zhang, Y., Sun, Z., Xu, K.: Veram: view-enhanced recurrent attention model for 3D shape classification. IEEE Trans. Vis. Comput. Graph. 25, 3244–3257 (2018)

3. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558–3565 (2019)

4. Giunchi, D., James, S., Steed, A.: 3D sketching for interactive model retrieval in virtual reality. In: Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-photorealistic Animation and Rendering, p. 1. ACM (2018)

5. Gomez-Donoso, F., Garcia-Garcia, A., Garcia-Rodriguez, J., Orts-Escolano, S., Cazorla, M.: LonchaNet: a sliced-based CNN architecture for real-time 3D object recognition. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 412–418. IEEE (2017)

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1. Cube of Space Sampling for 3D Model Retrieval;Applied Sciences;2021-11-24

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