MAT-Net: Medial Axis Transform Network for 3D Object Recognition
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Main track. Pages 774-781.
https://doi.org/10.24963/ijcai.2019/109
Keywords
- Computer Vision: Computer Vision
- Computer Vision: 2D and 3D Computer Vision
- Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
Abstract
3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.
@inproceedings{ijcai2019p109,
title = {MAT-Net: Medial Axis Transform Network for 3D Object Recognition},
author = {Hu, Jianwei and Wang, Bin and Qian, Lihui and Pan, Yiling and Guo, Xiaohu and Liu, Lingjie and Wang, Wenping},
booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI-19}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Sarit Kraus},
pages = {774--781},
year = {2019},
month = {8},
note = {Main track},
doi = {10.24963/ijcai.2019/109},
url = {https://doi.org/10.24963/ijcai.2019/109},
}
TY - CPAPER TI - MAT-Net: Medial Axis Transform Network for 3D Object Recognition AU - Hu, Jianwei AU - Wang, Bin AU - Qian, Lihui AU - Pan, Yiling AU - Guo, Xiaohu AU - Liu, Lingjie AU - Wang, Wenping T2 - Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2019 SP - 774 EP - 781 DO - 10.24963/ijcai.2019/109 UR - https://doi.org/10.24963/ijcai.2019/109 ER -Copied
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