Enhanced Surface Reconstruction and Semantic Segmentation of LiDAR Data in Autonomous Vehicle Perception Systems

Autonomous Vehicles (AVs) are redefining the transportation sector through their ability to navigate, make decisions, and complete autonomous tasks. For accurate perception and comprehension of the surroundings, the AVs heavily rely on segmenting high-resolution 3D point cloud data provided by Light Detection and Ranging (LiDAR) sensors for discerning objects and other environmental features. However, the current vehicular segmentation approaches experience shortcomings in data insufficiency, computational performance, and precision concerns. Hence, to counteract these limitations, the paper proposes a Semantic Segmentation approach using Ball-Pivoting Algorithm and U-Net (SSBU) that harmoniously combines the Ball-Pivoting surface reconstruction algorithm and 3D U-Net to enhance image characteristics, leading to highly accurate outcomes with optimal cost efficiency. This SSBU integration involves carrying out augmentations and pre-processing of the raw LiDAR data to transform them into voxels through the process of Voxelization. The voxels are further improved through a surface reconstruction technique that utilizes the Ball Pivoting Algorithm (BPA). The resulting 3D model is analyzed using 3D U-Net deep learning architecture for robust and real-time interpretation. The implementation has produced a mean Intersection Over Union (IoU) of 83.3 over the NuScenes data and 69.7 on the KITTI dataset, outperforming the state-of-the-art.