Edge computing-based 3D perception has received attention in intelligent transportation systems (ITS) because real-time monitoring of traffic candidates potentially strengthens Vehicle-to-Everything (V2X) orchestration. Thanks to the capability of precisely measuring the depth information on surroundings from LiDAR, the increasing studies focus on lidar-based 3D detection, which significantly promotes the development of 3D perception. Few methods met the real-time requirement of edge deployment because of high computation-intensive operations. Moreover, an inconsistency problem of object detection remains uncovered in the pointcloud domain due to large sparsity. This paper thoroughly analyses this problem, comprehensively roused by recent works on determining inconsistency problems in the image specialisation. Therefore, we proposed a 3D harmonic loss function to relieve the pointcloud based inconsistent predictions. Moreover, the feasibility of 3D harmonic loss is demonstrated from a mathematical optimization perspective. The KITTI dataset and DAIR-V2X-I dataset are used for simulations, and our proposed method considerably improves the performance than benchmark models. Further, the simulative deployment on an edge device (Jetson Xavier TX) validates our proposed model's efficiency. Our code is open-source and publicly available.
翻译:在智能运输系统(ITS)中,基于计算机的3D感知得到了关注,因为对交通候选人的实时监测有可能加强车辆到万物(V2X)的交织。由于能够精确测量来自LiDAR周围的深度信息,越来越多的研究侧重于基于Lidar的3D探测,这极大地促进了3D感知的发展。由于计算密集的操作,几乎没有方法满足边缘部署的实时要求。此外,由于大宽度,在点球域中仍然发现物体探测的不一致问题。本文透彻分析了这一问题,最近为确定图像专门化不一致问题而开展的工作全面激发了这一问题。因此,我们提议了3D协调损失功能,以缓解基于不一致预测的点球。此外,3D协调损失的可行性从数学优化角度得到证明。KITTI数据集和DAIR-V2X-I数据集被用于模拟,我们提议的公开方法比基准模型大大改进了性能。此外,在模型边缘装置(Jetson Xavier TX)上安装了模拟功能,我们提议的代码效率是公开的。