对融合特征敏感的三维点云识别与分割

朱安迪; 达飞鹏; 盖绍彦

doi:10.7652/xjtuxb202405006

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对融合特征敏感的三维点云识别与分割

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- 对融合特征敏感的三维点云识别与分割
- Recognition and Segmentation of 3D Point Clouds Sensitive to Fusion Features
- 西安交通大学学报 2024年58卷第5期页码：52-63
- 作者机构：
  
  1. 东南大学自动化学院,南京,210096
  2. 东南大学复杂工程系统测量与控制教育部重点实验室,南京,210096
- 作者简介：
  
  : 2023-09-01。作者简介: 朱安迪(1998—),女,硕士生
  达飞鹏(通信作者),男,教授,博士生导师。基金项目: 江苏省前沿引领技术基础研究专项项目(BK20192004C)。
- 基金信息：
  
  : 2023-09-01。作者简介: 朱安迪(1998—),女,硕士生;达飞鹏(通信作者),男,教授,博士生导师。基金项目: 江苏省前沿引领技术基础研究专项项目(BK20192004C)。
- DOI：10.7652/xjtuxb202405006
  中图分类号： TP391
- 网络首发：2024-05-10，
  
  纸质出版：2024
- 稿件说明：
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朱安迪, 达飞鹏, 盖绍彦. 对融合特征敏感的三维点云识别与分割[J]. 西安交通大学学报, 2024,58(5):52-63.

ZHU Andi, DA Feipeng, GAI Shaoyan. Recognition and Segmentation of 3D Point Clouds Sensitive to Fusion Features[J]. 2024, 58(5): 52-63.
朱安迪, 达飞鹏, 盖绍彦. 对融合特征敏感的三维点云识别与分割[J]. 西安交通大学学报, 2024,58(5):52-63. DOI： 10.7652/xjtuxb202405006.

ZHU Andi, DA Feipeng, GAI Shaoyan. Recognition and Segmentation of 3D Point Clouds Sensitive to Fusion Features[J]. 2024, 58(5): 52-63. DOI： 10.7652/xjtuxb202405006.

摘要

三维点云分类分割网络忽视了融合特征中的冗余信息

缺乏放大有效特征占比能力

不能充分挖掘特征的表达性。在CurveNet网络基础上

提出了一种能够筛选和丰富融合特征的方法

对点云的识别与分割效果达到了较先进水平。首先

提出了对融合特征具有筛选能力的特征选择子网络

利用结合了打分机制的TopK算子选出包含有效信息的融合特征

并且能够自适应地赋予被选特征权重。其次

在聚合曲线特征模块中增加了两个新分支

分别学习曲线内部点距离特征和曲线之间的线距离特征

通过快速通道相关性注意力机制提取各分支的内部相关性

增强了网络特征的信息描述能力。实验结果表明

分类任务在ModelNet40数据集上准确率达到了93.8%

分割任务在ShapeNet Part数据集上平均交并比达到了86.4%。与基准网络相比

分类效果与分割效果均有所提高

证明了算法的有效性。

Abstract

The 3D point cloud classification and segmentation networks ignore the redundant information in the fusion features

lack the ability to amplify the proportion of effective features

and cannot fully explore the expressiveness of features. Based on the CurveNet network

this paper proposes a method that can filter and enrich the fusion features

and the recognition and segmentation effect of point cloud reaches a relatively advanced level. Firstly

a feature selection subnetwork with filtering ability for fusion features is proposed

which combines TopK operator and a scoring mechanism to select fusion features containing valid information and adaptively assign weight to the selected features. Secondly

two new branches are added to the aggregation curve feature module

so as to learn the curve internal point distance features and the curve line distance features

respectively

and extract the internal correlation of each branch through the quick channel affinity attention mechanism

which enhances the information description ability of the network features. The experimental results show that the accuracy of the classification task on the ModelNet40 dataset reaches 93.8%

and the average intersection over union of the segmentation task on the ShapeNet Part dataset reaches 86.4%. Compared with the benchmark network

the classification effect and segmentation effect are improved

which proves the effectiveness of the proposed algorithm.

关键词

Keywords

references

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