Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1750-1758.DOI: 10.11772/j.issn.1001-9081.2022060952
• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles
Received:
2022-06-30
Revised:
2022-10-24
Accepted:
2022-10-26
Online:
2022-11-16
Published:
2023-06-10
Contact:
Hui WANG
About author:
LI Jianhong, born in 1995, M. S. Her research interests include computer graphics, artificial intelligence.
Supported by:
通讯作者:
王辉
作者简介:
王辉(1983—),男,河北石家庄人,副教授,博士,CCF会员,主要研究方向:计算机图形学、人工智能Email:wangh@stdu.edu.cn基金资助:
CLC Number:
Hui WANG, Jianhong LI. Few-shot recognition method of 3D models based on Transformer[J]. Journal of Computer Applications, 2023, 43(6): 1750-1758.
王辉, 李建红. 基于Transformer的三维模型小样本识别方法[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1750-1758.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060952
采样 点数 | ModelNet 40 | ShapeNet Core.v2 | ShapeNet Core_normal | |||
---|---|---|---|---|---|---|
3-way | 5-way | 3-way | 5-way | 3-way | 5-way | |
256 | 83.28 | 78.25 | 80.12 | 79.06 | 85.99 | 80.86 |
512 | 86.59 | 79.06 | 80.28 | 79.63 | 96.05 | 84.39 |
1 024 | 87.37 | 80.86 | 81.75 | 81.51 | 83.96 | 82.25 |
2 048 | 87.21 | 81.32 | 80.63 | 79.96 | 78.68 | 81.01 |
Tab. 1 Accuracy of 1-shot experiments at different sampling point numbers
采样 点数 | ModelNet 40 | ShapeNet Core.v2 | ShapeNet Core_normal | |||
---|---|---|---|---|---|---|
3-way | 5-way | 3-way | 5-way | 3-way | 5-way | |
256 | 83.28 | 78.25 | 80.12 | 79.06 | 85.99 | 80.86 |
512 | 86.59 | 79.06 | 80.28 | 79.63 | 96.05 | 84.39 |
1 024 | 87.37 | 80.86 | 81.75 | 81.51 | 83.96 | 82.25 |
2 048 | 87.21 | 81.32 | 80.63 | 79.96 | 78.68 | 81.01 |
数据集 | 3-way | 5-way |
---|---|---|
ModelNet 40 | 87.37 | 80.86 |
ShapeNet Core.v2 | 81.75 | 81.51 |
ShapeNet Core_normal | 83.96 | 82.25 |
Tab. 2 Accuracies of the proposed method of 1-shot experiments on ModelNet 40 and ShapeNet Core datasets
数据集 | 3-way | 5-way |
---|---|---|
ModelNet 40 | 87.37 | 80.86 |
ShapeNet Core.v2 | 81.75 | 81.51 |
ShapeNet Core_normal | 83.96 | 82.25 |
数据集 | K=1 | K=2 | K=5 | K=10 |
---|---|---|---|---|
ModelNet 40 | 80.86 | 81.25 | 83.77 | 84.21 |
ShapeNet Core_normal | 82.25 | 83.96 | 85.31 | 85.76 |
Tab. 3 Accuracies of 5-way K-shot experiments on ModelNet 40 and ShapeNet Core_normal datasets
数据集 | K=1 | K=2 | K=5 | K=10 |
---|---|---|---|---|
ModelNet 40 | 80.86 | 81.25 | 83.77 | 84.21 |
ShapeNet Core_normal | 82.25 | 83.96 | 85.31 | 85.76 |
数据集 | |||||
---|---|---|---|---|---|
ShapeNet Core.v2 | 79.33 | 82.32 | 80.57 | 79.18 | 79.62 |
ShapeNet Core_normal | 80.44 | 85.31 | 83.75 | 81.64 | 81.28 |
ModelNet 40 | 78.53 | 83.77 | 81.19 | 80.43 | 80.01 |
Tab. 4 Recognition accuracies at different λ values
数据集 | |||||
---|---|---|---|---|---|
ShapeNet Core.v2 | 79.33 | 82.32 | 80.57 | 79.18 | 79.62 |
ShapeNet Core_normal | 80.44 | 85.31 | 83.75 | 81.64 | 81.28 |
ModelNet 40 | 78.53 | 83.77 | 81.19 | 80.43 | 80.01 |
方法 | 5-way | 10-way | ||
---|---|---|---|---|
10-shot | 20-shot | 10-shot | 20-shot | |
DGCNN+cTree[ | 60.00 | 65.70 | 48.50 | 53.00 |
PointNet+cTree[ | 63.20 | 68.90 | 49.20 | 50.10 |
PointNet+Jigsaw[ | 66.50 | 69.20 | 56.90 | 66.50 |
本文方法 | 84.21 | 81.53 | 80.32 | 80.75 |
Tab. 5 Few-shot recognition accuracies of different deep learning methods on ModelNet 40 dataset
方法 | 5-way | 10-way | ||
---|---|---|---|---|
10-shot | 20-shot | 10-shot | 20-shot | |
DGCNN+cTree[ | 60.00 | 65.70 | 48.50 | 53.00 |
PointNet+cTree[ | 63.20 | 68.90 | 49.20 | 50.10 |
PointNet+Jigsaw[ | 66.50 | 69.20 | 56.90 | 66.50 |
本文方法 | 84.21 | 81.53 | 80.32 | 80.75 |
方法 | 5-way 1-shot | 5-way 5-shot |
---|---|---|
Dual-LSTM[ | 46.32 | 62.77 |
关系网络 | 70.27 | 72.13 |
无Transformer网络 | 35.56 | 36.92 |
本文方法 | 80.86 | 83.77 |
Tab. 6 Five-way accuracies of different few-shot recognition methods of 3D models on ModelNet 40 dataset
方法 | 5-way 1-shot | 5-way 5-shot |
---|---|---|
Dual-LSTM[ | 46.32 | 62.77 |
关系网络 | 70.27 | 72.13 |
无Transformer网络 | 35.56 | 36.92 |
本文方法 | 80.86 | 83.77 |
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