In order to plan rapid response during disasters, first responder agencies often adopt `bring your own device' (BYOD) model with inexpensive mobile edge devices (e.g., drones, robots, tablets) for complex video analytics applications, e.g., 3D reconstruction of a disaster scene. Unlike simpler video applications, widely used Multi-view Stereo (MVS) based 3D reconstruction applications (e.g., openMVG/openMVS) are exceedingly time consuming, especially when run on such computationally constrained mobile edge devices. Additionally, reducing the reconstruction latency of such inherently sequential algorithms is challenging as unintelligent, application-agnostic strategies can drastically degrade the reconstruction (i.e., application outcome) quality making them useless. In this paper, we aim to design a latency optimized MVS algorithm pipeline, with the objective to best balance the end-to-end latency and reconstruction quality by running the pipeline on a collaborative mobile edge environment. The overall optimization approach is two-pronged where: (a) application optimizations introduce data-level parallelism by splitting the pipeline into high frequency and low frequency reconstruction components and (b) system optimizations incorporate task-level parallelism to the pipelines by running them opportunistically on available resources with online quality control in order to balance both latency and quality. Our evaluation on a hardware testbed using publicly available datasets shows upto ~54% reduction in latency with negligible loss (~4-7%) in reconstruction quality.

以 BYOD 模型为基础，使用廉价的移动边缘设备（如无人机、机器人、平板电脑）进行复杂的视频分析应用，例如灾难现场的三维重建。本文通过设计延迟优化 MVS（多视点立体摄影）算法流水线，通过在协作移动边缘环境中运行该流水线来实现最佳平衡端到端延迟和重建质量，从而在可用资源上运行这些管道以平衡延迟和质量，其在硬件测试平台上的评估表明，延迟可以减少约 54％，而重建质量损失很小（约 4-7％）。

为灾害响应优化多视角三维重建的端到端延迟