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3D object detection plays a pivotal role in many applications, most notablyautonomous driving and robotics. These applications are commonly deployed onedge devices to promptly interact with the environment, and often require nearreal-time response. With limited computation power, it is challenging toexecute 3D detection on the edge using highly complex neural networks. Commonapproaches such as offloading to the cloud induce significant latency overheadsdue to the large amount of point cloud data during transmission. To resolve thetension between wimpy edge devices and compute-intensive inference workloads,we explore the possibility of empowering fast 2D detection to extrapolate 3Dbounding boxes. To this end, we present Moby, a novel system that demonstratesthe feasibility and potential of our approach. We design a transformationpipeline for Moby that generates 3D bounding boxes efficiently and accuratelybased on 2D detection results without running 3D detectors. Further, we devisea frame offloading scheduler that decides when to launch the 3D detectorjudiciously in the cloud to avoid the errors from accumulating. Extensiveevaluations on NVIDIA Jetson TX2 with real-world autonomous driving datasetsdemonstrate that Moby offers up to 91.9% latency improvement with modestaccuracy loss over state of the art.

Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge