# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 1 Introduction  
With the rapid development of deep learning technology, computer vision, a popular research direction in the artificial intelligence field, is now widely used in areas such as driverless and unmanned ground vehicles (UGV). The operation of intelligent systems such as unmanned vehicles relies heavily on sensors to capture information about the environment around the vehicle. The semantic segmentation of point clouds is a key aspect of scene understanding [1] for intelligent systems. It enables the segmentation of different objects in a scene by predicting the label of each point. Semantic segmentation [2] not only helps to identify and localize dynamic objects and determine road conditions, but it can also be used to construct a 3D semantic map of the environment [3]. However, the amount of point clouds collected by LiDAR (light detection and ranging) in real time is extremely large and spatially unevenly distributed. With the limited computing power and memory of unmanned vehicle systems, the existing segmentation models fail to effectively balance the relationship between model accuracy, computational efficiency, and real-time performance. The realization of real-time semantic segmentation of point clouds still faces great challenges. Currently, semantic segmentation methods are classified into three types depending on the way the point cloud data are represented: the raw point-based method [4, 5], the multi-view-based method [6, 7], and the voxelization-based method [8, 9]. The raw point-based method takes point cloud data directly as input and processes irregular point clouds by designing special convolution operations [5]. However, this method is less computationallyefficient and is generally used for small-scale point cloud computation in indoor settings; it is not considered effective for segmenting large-scale point clouds for outdoor scenes. Although the multi-view-based method meets the real-time needs of the system, the process of projecting 3D information into 2D can result in a significant loss of the original information and affect the segmentation accuracy. The voxelization-based approach divides the disordered point cloud into an ordered raster representation. This method has achieved better performance in terms of 3D data. Due to the sparsity of the point cloud data, it is inefficient to apply the voxelated point cloud data directly to dense convolutional neural networks. For this reason, many models have introduced sparse operations based on voxelization methods. Examples include SSCN [10], LatticeNet [11], and MinkowskiNet [8], but the segmentation accuracy of these models is limited by the real-time nature of the system. Therefore, current point cloud semantic segmentation models are unable to simultaneously meet the system's real-time requirements and increase the model accuracy. To achieve the real-time semantic segmentation of unmanned vehicle systems, we propose a lightweight, fully convolutional network (LFNet) based on an attention mechanism and a sparse tensor to process voxelized point cloud data. First, we propose a global feature-learning module (GFLM) based on the 3D-Unet structure, which improves the ability of features to describe important regions by learning the importance of spatial domains. In addition, we propose a multi-scale feature fusion module (MFFM), which enables the fusion of multi-scale semantic information by learning features at different scales. Additionally, to handle the large amount of point cloud information acquired by the intelligent system in real time and to reduce the model computation, we introduce a sparse tensor-based implementation. Finally, our model improves the mIoU (mean intersection over union) index by 6.4% and 5%, respectively, for the SemanticKITTI and NuScenes datasets compared with the existing models that can be applied in real time.