Moving-Least-Squares-Enhanced 3D Object Detection for 4D Millimeter-Wave Radar

Object detection is a critical task in autonomous driving. Currently, 3D object detection methods for autonomous driving primarily rely on stereo cameras and LiDAR, which are susceptible to adverse weather conditions and low lighting, resulting in limited robustness. In contrast, automotive mmWave radar offers advantages such as resilience to complex weather, independence from lighting conditions, and a low cost, making it a widely studied sensor type. Modern 4D millimeter-wave (mmWave) radar can provide spatial dimensions (x, y, z) as well as Doppler information, meeting the requirements for 3D object detection. However, the point cloud density of 4D mmWave radar is significantly lower than that of LiDAR in the case of short distances, and existing point cloud object detection methods struggle to adapt to such sparse data. To address this challenge, we propose a novel 4D mmWave radar point cloud object detection framework. First, we employ moving least squares (MLS) to densify multi-frame fused point clouds, effectively increasing the point cloud density. Next, we construct a 3D object detection network based on point pillar encoding and utilize an SSD detection head for detection on feature maps. Finally, we validate our method on the VoD dataset. Experimental results demonstrate that our proposed framework outperforms comparative methods, and the MLS-based point cloud densification method significantly enhances the object detection performance.