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A self-attention based global feature enhancing network for semantic segmentation of large-scale urban street-level point clouds
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-08-22 , DOI: 10.1016/j.jag.2022.102974
Qi Chen, Zhenxin Zhang, Siyun Chen, Siyuan Wen, Hao Ma, Zhihua Xu

Point clouds of large-scale urban street scenes contain large quantities of object categories and rich semantic information. The semantic segmentation is the basis and key to subsequent essential applications, such as digital twin engineering and city information model. The global feature of point clouds in large-scale scenes can provide long-range context information, which is critical to high-quality semantic segmentation. However, the learning of global spatial saliency considering class label constraints is often ignored in the feature representation of some deep learning models. With regard to this, we propose a Global Feature Self-Attention Encoding (GFSAE) module and a Weighted Semantic Mapping (WSM) module to make the semantic segmentation model of point clouds in large-scale urban street scene focus more on the global salient feature expression by self-attention enhancement channel by channel and take into account the constraints of semantic categories to learn a better semantic segmentation model for urban street scenes. The experiments are performed on the Semantic3D dataset and our own collected vehicle Mobile Laser Scanning (MLS) point cloud dataset. The segmentation results show that the GFSAE and the WSM proposed by us can improve the semantic segmentation of point clouds in large-scale urban street scenes and prove the effectiveness of our model compared with other state-of-the-art methods.



中文翻译:

一种基于自注意力的全局特征增强网络,用于大规模城市街道级点云的语义分割

大规模城市街景点云包含大量的对象类别和丰富的语义信息。语义分割是数字孪生工程和城市信息模型等后续基本应用的基础和关键。大规模场景中点云的全局特征可以提供远程上下文信息,这对于高质量的语义分割至关重要。然而,考虑类标签约束的全局空间显着性学习在一些深度学习模型的特征表示中经常被忽略。对此,我们提出了全局特征自注意力编码(GFSAE)模块和加权语义映射(WSM)模块,使大规模城市街景中点云的语义分割模型更加关注自注意力的全局显着特征表达逐通道增强并考虑语义类别的约束,以学习更好的城市街景语义分割模型。实验是在 Semantic3D 数据集和我们自己收集的车辆移动激光扫描 (MLS) 点云数据集上进行的。分割结果表明,我们提出的 GFSAE 和 WSM 可以改善大规模城市街景中点云的语义分割,并与其他最先进的方法相比,证明了我们的模型的有效性。

更新日期:2022-08-22
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