VeloVox: A Low-Cost and Accurate 4D Object Detector with Single-Frame Point Cloud of Livox LiDAR

Tao Ma, Zhiwei Zheng, Hongbin Zhou, Xinyu Cai, Xuemeng Yang, Yikang Li, Botian Shi, Hongsheng Li

Abstract: Combining motion prediction in LiDAR-based 3D object detection is an effective method for improving overall accuracy, especially the downstream autonomous driving tasks. The recent development of low-cost LiDARs (e.g. Livox LiDAR) enables us to explore such 4D perception systems with a lower budget and higher performance. In this paper, we propose a 4D object detector, VeloVox, to establish accurate object detection and velocity estimation with a single-frame point cloud of Livox LiDAR. Based on the non-repetitive scanning pattern and point-level temporal nature, we propose a two-stage module to enhance the spatial-temporal point feature interaction along the time dimension. The aggregated feature also benefits a more accurate proposal refinement. To demonstrate the performance, comparison of VeloVox with several SOTA detector based baselines is evaluated on our in-house dataset and synthesized dataset built under Carla simulation. Code will be released at https://github.com/PJLab-ADG/VeloVox.