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Extraction of local structure information of point clouds through space-filling curve for semantic segmentation
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-26 , DOI: 10.1016/j.jag.2022.103027
Xueyong Xiang, Li Wang, Wenpeng Zong, Guangyun Li

The point cloud semantic segmentation network based on point-wise multi-layer perceptron (MLP) has gained extensive applications because of its end-to-end advantages. However, there exist two major limitations for this type of network: (1) In a neighborhood ball, the relative features between the central point and its neighboring point are not adequately exploited. (2) Only the relative features in the neighborhood ball are extracted, but the overall morphological information of the neighborhood ball is ignored. To overcome the limitations, this paper proposes two-fold improvements on local structure information extraction: (1) The relative angular feature is added and combined with other initial features as the input of the model to exploit the relative geometric features between points to the greatest extent. (2) The unordered points in the neighborhood ball are reordered based on the space-filling curve (SFC) and then fed into the MLP to extract the overall structure information of the neighborhood ball. And a semantic segmentation model is developed based on the two proposed feature extraction modules and U-Net, which is evaluated on two public datasets. The experimental result has demonstrated that the two proposed feature extraction modules can effectively extract geometric information in the point cloud and the proposed semantic segmentation model has strong semantic recognition capability for objects with complex morphologies. The mean intersection over union (mIoU) of the proposed model reached 70.6% and 47.8% for the Semantic 3D and Semantic KITTI datasets, respectively. Besides, the proposed model achieves real-time segmentation with only four encoder layers.



中文翻译:

通过空间填充曲线提取点云局部结构信息用于语义分割

基于逐点多层感知器(MLP)的点云语义分割网络因其端到端的优势而获得了广泛的应用。然而,这种类型的网络存在两个主要限制:(1)在邻域球中,中心点与其相邻点之间的相对特征没有得到充分利用。(2)只提取邻域球中的相对特征,而忽略邻域球的整体形态信息。为了克服这些限制,本文对局部结构信息提取提出了两方面的改进:(1)增加了相对角度特征,并结合其他初始特征作为模型的输入,最大限度地利用点之间的相对几何特征。程度。(2)邻域球中的无序点根据空间填充曲线(SFC)重新排序,然后输入MLP,提取邻域球的整体结构信息。并基于两个提出的特征提取模块和 U-Net 开发了语义分割模型,并在两个公共数据集上进行了评估。实验结果表明,所提出的两个特征提取模块能够有效地提取点云中的几何信息,所提出的语义分割模型对具有复杂形态的物体具有较强的语义识别能力。对于 Semantic 3D 和 Semantic KITTI 数据集,所提出模型的平均交集(mIoU)分别达到了 70.6% 和 47.8%。除了,

更新日期:2022-09-26
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