Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (9): 3003-3010.DOI: 10.11772/j.issn.1001-9081.2024091254
• Multimedia computing and computer simulation • Previous Articles
Weigang LI1,2, Jiale SHAO1(), Zhiqiang TIAN2
Received:
2024-09-05
Revised:
2024-10-16
Accepted:
2024-10-18
Online:
2024-10-31
Published:
2025-09-10
Contact:
Jiale SHAO
About author:
LI Weigang, born in 1977, Ph. D., professor. His research interests include industrial process control, artificial intelligence, machine learning.Supported by:
通讯作者:
邵佳乐
作者简介:
李维刚(1977—),男,湖北咸宁人,教授,博士,主要研究方向:工业过程控制、人工智能、机器学习; 深度学习、点云数据处理基金资助:
CLC Number:
Weigang LI, Jiale SHAO, Zhiqiang TIAN. Point cloud classification and segmentation network based on dual attention mechanism and multi-scale fusion[J]. Journal of Computer Applications, 2025, 45(9): 3003-3010.
李维刚, 邵佳乐, 田志强. 基于双注意力机制和多尺度融合的点云分类与分割网络[J]. 《计算机应用》唯一官方网站, 2025, 45(9): 3003-3010.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2024091254
方法 | 输入 | mAcc/% | OA/% | 运算量/GFLOPs |
---|---|---|---|---|
VoxNet | voxel | 83.0 | 85.9 | — |
MVCNN | image | — | 90.1 | — |
PointNet | point | 86.2 | 89.2 | 0.440 |
PointNet++ | point | 88.3 | 90.7 | 0.870 |
DGCNN | point | 90.2 | 92.9 | 2.450 |
PCNN | point | 88.1 | 92.2 | — |
PointConv | point | — | 92.5 | — |
PCT | point | — | 93.2 | 2.320 |
PointWeb | point | 89.4 | 92.3 | — |
Point Transformer | point | — | 92.8 | — |
JGEKD | point | 90.9 | 93.4 | — |
本文方法 | point | 91.3 | 93.5 | 2.204 |
Tab. 1 Comparison of classification experimental results on ModelNet40 dataset
方法 | 输入 | mAcc/% | OA/% | 运算量/GFLOPs |
---|---|---|---|---|
VoxNet | voxel | 83.0 | 85.9 | — |
MVCNN | image | — | 90.1 | — |
PointNet | point | 86.2 | 89.2 | 0.440 |
PointNet++ | point | 88.3 | 90.7 | 0.870 |
DGCNN | point | 90.2 | 92.9 | 2.450 |
PCNN | point | 88.1 | 92.2 | — |
PointConv | point | — | 92.5 | — |
PCT | point | — | 93.2 | 2.320 |
PointWeb | point | 89.4 | 92.3 | — |
Point Transformer | point | — | 92.8 | — |
JGEKD | point | 90.9 | 93.4 | — |
本文方法 | point | 91.3 | 93.5 | 2.204 |
方法 | 不同类别的IoU | Cls.mIoU | mIoU | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
飞机 | 包 | 帽子 | 车 | 椅子 | 耳机 | 吉他 | 刀 | 灯 | 电脑 | 摩托 | 杯子 | 手枪 | 火箭 | 滑板 | 桌子 | |||
PointNet | 83.4 | 78.7 | 82.5 | 74.9 | 89.6 | 73.0 | 91.5 | 85.9 | 80.8 | 95.3 | 65.2 | 93.0 | 81.2 | 57.9 | 72.8 | 80.6 | 80.4 | 83.7 |
PointNet++ | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6 | 81.9 | 85.1 |
DGCNN | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 | 82.3 | 85.2 |
LDGCNN | 84.0 | 83.0 | 84.9 | 78.4 | 90.6 | 74.4 | 91.0 | 88.1 | 83.4 | 95.8 | 67.4 | 94.9 | 82.3 | 59.2 | 76.0 | 81.9 | 82.2 | 85.1 |
PCNN | 82.4 | 80.1 | 85.5 | 79.5 | 90.8 | 73.2 | 91.3 | 86.0 | 85.0 | 95.7 | 73.2 | 94.8 | 83.3 | 51.0 | 75.0 | 81.8 | 81.8 | 85.1 |
PointASNL | 84.1 | 84.7 | 87.9 | 79.7 | 92.2 | 73.7 | 91.0 | 87.2 | 84.2 | 95.8 | 74.4 | 95.2 | 81.0 | 63.0 | 76.3 | 83.2 | 83.3 | 86.1 |
本文方法 | 83.3 | 85.3 | 90.4 | 77.6 | 90.8 | 78.1 | 91.4 | 88.0 | 85.1 | 95.9 | 72.5 | 95.3 | 82.2 | 63.7 | 77.8 | 83.1 | 83.8 | 86.4 |
Tab. 2 Comparison of component segmentation performance of different methods on ShapeNet dataset
方法 | 不同类别的IoU | Cls.mIoU | mIoU | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
飞机 | 包 | 帽子 | 车 | 椅子 | 耳机 | 吉他 | 刀 | 灯 | 电脑 | 摩托 | 杯子 | 手枪 | 火箭 | 滑板 | 桌子 | |||
PointNet | 83.4 | 78.7 | 82.5 | 74.9 | 89.6 | 73.0 | 91.5 | 85.9 | 80.8 | 95.3 | 65.2 | 93.0 | 81.2 | 57.9 | 72.8 | 80.6 | 80.4 | 83.7 |
PointNet++ | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6 | 81.9 | 85.1 |
DGCNN | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 | 82.3 | 85.2 |
LDGCNN | 84.0 | 83.0 | 84.9 | 78.4 | 90.6 | 74.4 | 91.0 | 88.1 | 83.4 | 95.8 | 67.4 | 94.9 | 82.3 | 59.2 | 76.0 | 81.9 | 82.2 | 85.1 |
PCNN | 82.4 | 80.1 | 85.5 | 79.5 | 90.8 | 73.2 | 91.3 | 86.0 | 85.0 | 95.7 | 73.2 | 94.8 | 83.3 | 51.0 | 75.0 | 81.8 | 81.8 | 85.1 |
PointASNL | 84.1 | 84.7 | 87.9 | 79.7 | 92.2 | 73.7 | 91.0 | 87.2 | 84.2 | 95.8 | 74.4 | 95.2 | 81.0 | 63.0 | 76.3 | 83.2 | 83.3 | 86.1 |
本文方法 | 83.3 | 85.3 | 90.4 | 77.6 | 90.8 | 78.1 | 91.4 | 88.0 | 85.1 | 95.9 | 72.5 | 95.3 | 82.2 | 63.7 | 77.8 | 83.1 | 83.8 | 86.4 |
方法 | mIoU | mAcc |
---|---|---|
PointNet | 41.1 | 48.9 |
PointNet++ | 50.6 | — |
DGCNN | 47.0 | — |
PCNN | 57.3 | 63.9 |
本文方法 | 59.1 | 65.3 |
Tab. 3 Comparison of semantic segmentation experimental results using different methods
方法 | mIoU | mAcc |
---|---|---|
PointNet | 41.1 | 48.9 |
PointNet++ | 50.6 | — |
DGCNN | 47.0 | — |
PCNN | 57.3 | 63.9 |
本文方法 | 59.1 | 65.3 |
权重矩阵数 | OA/% | 权重矩阵数 | OA/% |
---|---|---|---|
2 | 92.2 | 8 | 93.4 |
4 | 92.4 | 16 | 93.0 |
Tab. 4 OAs with different numbers of weight matrices
权重矩阵数 | OA/% | 权重矩阵数 | OA/% |
---|---|---|---|
2 | 92.2 | 8 | 93.4 |
4 | 92.4 | 16 | 93.0 |
方法 | mAcc | OA |
---|---|---|
PointNet++ | 88.3 | 90.7 |
+GAC | 90.4 | 92.4 |
+GAC+DAM | 90.7 | 92.8 |
+GAC+MSFF | 90.8 | 93.1 |
本文方法 | 91.3 | 93.5 |
Tab. 5 Results of ablation experiments on ModelNet40 dataset
方法 | mAcc | OA |
---|---|---|
PointNet++ | 88.3 | 90.7 |
+GAC | 90.4 | 92.4 |
+GAC+DAM | 90.7 | 92.8 |
+GAC+MSFF | 90.8 | 93.1 |
本文方法 | 91.3 | 93.5 |
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