结合位置关系卷积与深度残差网络的三维点云识别与分割

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结合位置关系卷积与深度残差网络的三维点云识别与分割

更新时间：2025-07-09

- 结合位置关系卷积与深度残差网络的三维点云识别与分割
- Issue 5, Pages: 182-193(2023)
- 作者机构：
  
  1. 兰州交通大学电子与信息工程学院
  2. 兰州交通大学测绘与地理信息学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183;TP391.41
- Published：2023
- 稿件说明：
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[J]. 2023, (5): 182-193.
DOI：

[J]. 2023, (5): 182-193. DOI：

摘要

针对现有点云识别与分割算法因忽视点的位置特征和局部几何特征关系而导致难以捕获具有鉴别力的局部几何信息的问题，提出基于位置关系深度残差神经网络的三维点云识别与分割算法。将原始点云嵌入到高维空间并获取其高维特征；将点云的高维特征输入位置关系卷积实现局部邻域内当前点特征与位置几何特征的信息交流，并通过深度残差模块强化提取到的深层语义特征，分层重复以上步骤可逐步得到点云的高级上下文语义特征；通过全连接层与解码器，得到点云的识别与分割结果。实验结果表明，所提算法在ModelNet40点云分类数据集的识别精度达到了93.9%

在ShapeNet Part点云部件语义分割数据集的平均交并比达到了86.0%。所提算法能够提取三维点云的关键特征信息，具有较好的三维点云识别与分割能力。

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references

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