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基于注意力机制的三维点云模型对应关系计算

杨军 高志明 李金泰 张琛

杨军, 高志明, 李金泰, 张琛. 基于注意力机制的三维点云模型对应关系计算[J]. 西南交通大学学报, 2024, 59(5): 1184-1193. doi: 10.3969/j.issn.0258-2724.20220682
引用本文: 杨军, 高志明, 李金泰, 张琛. 基于注意力机制的三维点云模型对应关系计算[J]. 西南交通大学学报, 2024, 59(5): 1184-1193. doi: 10.3969/j.issn.0258-2724.20220682
YANG Jun, GAO Zhiming, LI Jintai, ZHANG Chen. Correspondence Calculation of Three-Dimensional Point Cloud Model Based on Attention Mechanism[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1184-1193. doi: 10.3969/j.issn.0258-2724.20220682
Citation: YANG Jun, GAO Zhiming, LI Jintai, ZHANG Chen. Correspondence Calculation of Three-Dimensional Point Cloud Model Based on Attention Mechanism[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1184-1193. doi: 10.3969/j.issn.0258-2724.20220682

基于注意力机制的三维点云模型对应关系计算

doi: 10.3969/j.issn.0258-2724.20220682
基金项目: 国家自然科学基金项目(42261067,61862039);兰州市人才创新创业项目(2020-RC-22)
详细信息
    作者简介:

    杨军(1973—),男,教授,博士生导师,研究方向为三维模型空间分析、遥感大数据智能解译、机器学习等,E-mail:yangj@mail.lzjtu.cn

  • 中图分类号: TP391.4

Correspondence Calculation of Three-Dimensional Point Cloud Model Based on Attention Mechanism

  • 摘要:

    针对现有深度学习方法计算非刚性点云模型间稠密对应关系时精度不高,且算法泛化能力较差的问题,提出一种基于特征序列注意力机制的无监督三维点云模型对应关系计算新方法. 首先,使用特征提取模块提取输入点云模型对的特征;然后,通过Transformer模块捕获自注意力和交叉注意力,学习共同上下文信息,并由对应关系预测模块生成软映射矩阵;最后,重构模块根据得到的软映射矩阵重构点云模型,并利用无监督损失函数完成训练. 在FAUST、SHREC’19和SMAL数据集上的实验结果表明,本算法的平均对应误差分别为5.1、5.8和5.4,均低于3D-CODED、Elementary Structures和CorrNet3D经典算法;本算法所计算的非刚性三维点云模型间对应关系准确率更高,且具有更强的泛化能力.

     

  • 图 1  总体网络框架

    Figure 1.  Overall network framework

    图 2  Transformer模块

    Figure 2.  Transformer module

    图 3  点云重构模块

    Figure 3.  Point cloud reconstruction module

    图 4  对应关系误差曲线

    Figure 4.  Correspondence error curves

    表  1  FAUST数据集上各方法的实验结果对比

    Table  1.   Comparison of experimental results of methods onFAUST dataset

    示例 源模型 Groud-truth 3D-CODED Elementary Structures CorrNet3D 本文算法
    1
    2
    下载: 导出CSV

    表  2  SHREC’19数据集上各方法的实验结果对比

    Table  2.   Comparison of experimental results of methods on SHREC’19 dataset

    示例 源模型 Groud-truth 3D-CODED Elementary Structures CorrNet3D 本文算法
    3
    4
    下载: 导出CSV

    表  3  SMAL数据集上各方法的实验结果对比

    Table  3.   Comparison of experimental results of methods on SMAL dataset

    示例 源模型 Groud-truth 3D-CODED Elementary Structures CorrNet3D 本文算法
    5
    6
    7
    下载: 导出CSV

    表  4  不同方法平均对应误差

    Table  4.   Average correspondence errors of different methods

    数据集 3D-
    CODED
    Elementary
    Structures
    CorrNet
    3D
    本文
    算法
    FAUST 7.2 6.6 5.3 5.1
    SHREC’19 8.1 7.6 6.9 5.8
    SMAL 6.8 6.4 5.7 5.4
    下载: 导出CSV

    表  5  Transformer模块消融实验误差

    Table  5.   Errors of ablation experiment on Transformer module

    数据集 无 Transformer 模块 有 Transformer 模块
    FAUST 5.6 5.1
    SHREC’19 6.1 5.8
    SMAL 7.6 5.4
    下载: 导出CSV

    表  6  对应关系预测模块消融实验误差

    Table  6.   Errors of ablation experiment on correspondence prediction module

    数据集 无对应关系
    预测模块
    有对应关系
    预测模块
    FAUST 6.1 5.1
    SHREC’19 7.3 5.8
    SMAL 8.0 5.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-12
  • 修回日期:  2022-12-30
  • 网络出版日期:  2024-06-17
  • 刊出日期:  2023-01-12

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