Robust multi-task learning network for complex LiDAR point cloud data preprocessing

Abstract

The utilization of 3D point clouds acquired via Light Detection and Ranging (LiDAR) is widespread in the fields of autonomous driving, satellite remote sensing, and spatial mapping. However, due to hardware limitations of the laser launch system and environmental interferences, the quality of point cloud data obtained through various types of LiDAR is often poor in real-world scenarios, containing extraneous noise and irrelevant data points. This poses a challenge for subsequent point cloud downstream tasks that (e.g., point cloud detection, recognition and tracking) require high-quality data. We propose a robust multi-task learning network for pre-processing LiDAR data. Our approach utilizes a shared PointNet encoder and three branching networks that perform denoising, single-object segmentation, and completion. The denoising branch network incorporates the traditional model based on geometric projection, leveraging the dual-driven approach of data and model for better capturing the characteristics of the point cloud. Regarding the segmentation branch network, we integrate an attention mechanism module suitable for single-object segmentation, enabling the network to better extract the point cloud features of complex objects. For the completion branch network, we employ a folded network structure to achieve a coarse-to-fine completion effect of the point cloud. We discuss the training methods, that is, end-to-end and step-by-step methods, which can enhance flexibility during the training and usage phase. Our proposed network outperforms prior state-of-the-art approaches in all three tasks on both ShapeNet and simulated point cloud data of the sea face scene while demonstrating superior robustness.