论文标题
使用多尺度稀疏卷积神经网络的点云语义分割
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network
论文作者
论文摘要
近年来，随着计算资源和激光雷达的开发，点云语义细分吸引了许多研究人员。对于点云的稀疏性，尽管已经有一种处理稀疏卷积的方法，但不考虑多尺度功能。在这封信中，我们提出了一个基于多尺度稀疏卷积的特征提取模块和基于信道注意的特征选择模块，并基于此建立点云分割网络框架。通过引入多尺度稀疏卷积，该网络可以根据具有不同尺寸的卷积内核捕获更丰富的特征信息，从而改善了点云分割的分割结果。斯坦福大学大规模3-D室内空间（S3DIS）数据集和室外数据集（Semantickitti）的实验结果表明了所提出的Mothod的有效性和优越性。
In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers. For the sparsity of point clouds, although there is already a way to deal with sparse convolution, multi-scale features are not considered. In this letter, we propose a feature extraction module based on multi-scale sparse convolution and a feature selection module based on channel attention and build a point cloud segmentation network framework based on this. By introducing multi-scale sparse convolution, the network could capture richer feature information based on convolution kernels with different sizes, improving the segmentation result of point cloud segmentation. Experimental results on Stanford large-scale 3-D Indoor Spaces(S3DIS) dataset and outdoor dataset(SemanticKITTI), demonstrate effectiveness and superiority of the proposed mothod.
