PLSTMNet: A New Neural Network for Segmentation of Point Cloud

Point cloud is a significant data which is collected from various sensors. but due to the unordered and irregular data form, an efficient and fully exploitation of raw point cloud is still challenging. PointNet provides us a promising network architecture to utilize the raw point cloud directly, which can considered to be a global descriptor of point cloud in an efficient way. Nevertheless, the information contained in local geometry is ignored by PointNet, if properly used, which can lead to a more accurate result. In this paper, LSTM network is employed to acquire the relationship contained in the point cloud, and we put forward an innovative deep-learning network, termed PLSTMNet. In experiments, we apply our PLSTMNet in the semantic segmentation task, and achieve much better performance than PointNet. The result shows the potential of LSTM in processing of point cloud.