  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.
