3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics. These applications are commonly deployed on edge devices to promptly interact with the environment, and often require near real-time response. With limited computation power, it is challenging to execute 3D detection on the edge using highly complex neural networks. Common approaches such as offloading to the cloud induce significant latency overheads due to the large amount of point cloud data during transmission. To resolve the tension between wimpy edge devices and compute-intensive inference workloads, we explore the possibility of empowering fast 2D detection to extrapolate 3D bounding boxes. To this end, we present Moby, a novel system that demonstrates the feasibility and potential of our approach. We design a transformation pipeline for Moby that generates 3D bounding boxes efficiently and accurately based on 2D detection results without running 3D detectors. Further, we devise a frame offloading scheduler that decides when to launch the 3D detector judiciously in the cloud to avoid the errors from accumulating. Extensive evaluations on NVIDIA Jetson TX2 with real-world autonomous driving datasets demonstrate that Moby offers up to 91.9% latency improvement with modest accuracy loss over state of the art.

翻译：三维目标检测在诸多应用中发挥着关键作用，最显著的是自动驾驶和机器人领域。这些应用通常部署在边缘设备上，以实时与环境交互，且常常需要接近实时的响应速度。由于计算能力有限，在边缘设备上使用高度复杂的神经网络执行三维检测极具挑战性。常见方法如将任务卸载至云端，会因传输过程中的海量点云数据而产生显著的延迟开销。为化解弱边缘设备与计算密集型推理工作负载之间的矛盾，我们探索了利用快速二维检测来推断三维边界框的可能性。为此，我们提出Moby系统，这是一个新颖的系统，验证了我们方法的可行性与潜力。我们为Moby设计了一个转换流水线，能够基于二维检测结果高效且准确地生成三维边界框，而无需运行三维检测器。此外，我们设计了一个帧卸载调度器，用于审慎决策何时在云端启动三维检测器，以避免误差累积。基于NVIDIA Jetson TX2平台使用真实自动驾驶数据集进行的广泛评估表明，与现有先进技术相比，Moby在精度略有损失的情况下，可提升高达91.9%的延迟性能。