  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.
