超点图框架下融合双向注意力机制的点云语义分割方法研究
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河海大学 地球科学与工程学院

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江苏省研究生科研与实践创新计划项目(编号:SJCX24_0201),国家自然科学基金(编号:42071440)


Semantic segmentation of point cloud by incorporating Two-way attention mechanism in superpoint graph framework
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School of Earth Sciences and Engineering,Hohai University,Nanjing

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    摘要:

    针对点云语义分割中,传统的图神经网络方法存在监督精度要求高、节点标签传递只能单向、未考虑全局信息等缺陷,本文提出一种基于双向注意力机制的点云语义分割方法。首先,将点云超分割为超点并建立超点图,从而将点云分类问题引入超点图网络框架中。然后,利用双向注意力模块,交替关注超点,根据邻接超点的权重更新超点特征,实现信息的双向传递。与以往的图池化方法不同,本文同时引入最大池化和平均池化,并将池化特征结合。最后,使用公开数据集Semantic3D进行训练和实验。结果表明,本文提出的方法可以有效地对标注误差进行纠正,同时耦合局部特征和长程信息,数据集的平均交互比(mIoU)和总体准确度(oAcc)分别为75.4%和95.1%,相比现有方法体现出更完善的标签传递机制和更高的分类精度。

    Abstract:

    Aiming at the shortcomings of traditional graph neural network methods in point cloud semantic segmentation, such as high supervision accuracy requirement, node label transfer can only be unidirectional, and global information is not taken into account, this paper proposes a point cloud semantic segmentation method based on bidirectional attention mechanism. The algorithm segments the point cloud into superpoints and constructs a superpoint graph thus introducing the point cloud classification problem into the superpoint graph framework. After that, using the two-way attention module, it alternately pays attention to the superpoints and updates the superpoint features according to the weights of the neighboring superpoints, so as to achieve the two-way transfer of information. Also compared to previous graph pooling methods, this paper applies both maximum pooling and average pooling and combines the pooled features. In this paper, we use the public dataset Semantic3D for training and experiments, and the results show that the proposed method can effectively correct the annotation error and combine the long-range information, and the mIoU and oAcc of the dataset are 75.4% and 95.1%, respectively, which reflect better label delivery mechanism and higher classification accuracy compared with state of art methods.

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李国立,陈焱明,夏家康,邹新灿.超点图框架下融合双向注意力机制的点云语义分割方法研究[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-10-24
  • 最后修改日期:2024-12-25
  • 录用日期:2024-12-26

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