Fast Context-Awareness Encoder for LiDAR Point Semantic Segmentation

Abstract

A LiDAR sensor is a valuable tool for environmental perception as it can generate 3D point cloud data with reflectivity and position information by reflecting laser beams. However, it cannot provide the meaning of each point cloud cluster, so many studies focus on identifying semantic information about point clouds. This paper explores point cloud segmentation and presents a lightweight convolutional network called Fast Context-Awareness Encoder (FCAE), which can obtain semantic information about the point cloud cluster at different levels. The surrounding features of points are extracted as local features through the local context awareness network, then combined with global features, which are highly abstracted from the local features, to obtain more accurate semantic segmentation of the discrete points in space. The proposed algorithm has been compared and verified against other semantic KITTI data algorithms and has achieved state-of-the-art performance. Due to its ability to note fine-grained features on the z-axis in space, the algorithm shows higher prediction accuracy for certain types of objects. Moreover, the training and validation time is short, and the algorithm can meet high real-time requirements for 3D perception tasks.