Computer Engineering College, Jimei University, Xiamen 361021, China.
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China.
Sensors (Basel). 2019 Oct 7;19(19):4329. doi: 10.3390/s19194329.
Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial Aggregation Net (SAN) for point cloud semantic segmentation. SAN is based on multi-directional convolution scheme that utilizes the spatial structure information of point cloud. Firstly, Octant-Search is employed to capture the neighboring points around each sampled point. Secondly, we use multi-directional convolution to extract information from different directions of sampled points. Finally, max-pooling is used to aggregate information from different directions. The experimental results conducted on ScanNet database show that the proposed SAN has comparable results with state-of-the-art algorithms such as PointNet, PointNet++, and PointSIFT, etc. In particular, our method has better performance on flat, small objects, and the edge areas that connect objects. Moreover, our model has good trade-off in segmentation accuracy and time complexity.
三维点云的语义分割在自动驾驶、三维地图和智慧城市等领域中起着至关重要的作用。最近的研究如 PointSIFT 表明，空间结构信息可以提高语义分割的性能。受此现象启发，我们提出了用于点云语义分割的空间聚合网络 (SAN)。SAN 基于多方向卷积方案，利用点云的空间结构信息。首先，采用八叉树搜索来捕获每个采样点周围的邻域点。其次，我们使用多方向卷积从采样点的不同方向提取信息。最后，采用最大池化操作来聚合来自不同方向的信息。在 ScanNet 数据库上进行的实验结果表明，所提出的 SAN 在与 PointNet、PointNet++ 和 PointSIFT 等先进算法的比较中具有相当的结果。特别是，我们的方法在平坦、小物体以及连接物体的边缘区域上具有更好的性能。此外，我们的模型在分割精度和时间复杂度之间具有良好的权衡。
