The semantic segmentation of small objects in point clouds is currently one of the most demanding tasks in photogrammetry and remote sensing applications. Multi-resolution feature extraction and fusion can significantly enhance the ability of object classification and segmentation, so it is widely used in the image field. For this motivation, we propose a point cloud semantic segmentation network based on multi-scale feature fusion (MSSCN) to aggregate the feature of a point cloud with different densities and improve the performance of semantic segmentation. In our method, random downsampling is first applied to obtain point clouds of different densities. A Spatial Aggregation Net (SAN) is then employed as the backbone network to extract local features from these point clouds, followed by concatenation of the extracted feature descriptors at different scales. Finally, a loss function is used to combine the different semantic information from point clouds of different densities for network optimization. Experiments were conducted on the S3DIS and ScanNet datasets, and our MSSCN achieved accuracies of 89.80% and 86.3%, respectively, on these datasets. Our method showed better performance than the recent methods PointNet, PointNet++, PointCNN, PointSIFT, and SAN.

中文翻译：

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基于多尺度特征融合的软云语义分割网络

当前，点云中小对象的语义分割是摄影测量和遥感应用程序中最苛刻的任务之一。多分辨率特征提取和融合可以显着增强对象分类和分割的能力，因此在图像领域得到了广泛的应用。为此，我们提出了一种基于多尺度特征融合（MSSCN）的点云语义分割网络，以聚合具有不同密度的点云特征，提高了语义分割的性能。在我们的方法中，首先应用随机下采样以获得不同密度的点云。然后，将空间聚合网（SAN）用作骨干网，以从这些点云中提取局部特征，然后以不同比例级联提取的特征描述符。最后，使用损失函数将来自不同密度的点云的不同语义信息进行组合，以进行网络优化。在S3DIS和ScanNet数据集上进行了实验，我们的MSSCN在这些数据集上分别达到了89.80％和86.3％的准确度。与最近的方法PointNet，PointNet ++，PointCNN，PointSIFT和SAN相比，我们的方法显示出更好的性能。