Argus: Multi-view LiDAR Point Cloud Fusion for Enhancing Vehicle Detection in Auto Driving

The environmental perception of unmanned ground vehicles UGVs direct-ly impacts decisions like path planning and obstacle avoidance, with vehi-cle detection being critical for autonomous driving. LiDAR provides high-precision point clouds but suffers from sparse density and self-occlusion, often resulting in incomplete vehicle point clouds that hinder detection performance. To address this, we propose Argus, a multiview registration and completion model that fuses multi-frame point clouds of surrounding vehicles. Argus achieves multi-view fusion through a self-attention-based cumulative registration module and a coarse-to-fine residual completion module, refining vehicle point clouds using grid residual layers and a multi-layer perceptron. Compared to single-view point clouds, Argus produces denser and more complete vehicle shapes, serving as an independent plug-in to enhance detection methods. Experiments on the KITTI dataset show that Argus improves downstream vehicle detection performance.