  Image-based three-dimensional (3D) reconstruction utilizes a set of photos to
build 3D model and can be widely used in many emerging applications such as
augmented reality (AR) and disaster recovery. Most of existing 3D
reconstruction methods require a mobile user to walk around the target area and
reconstruct objectives with a hand-held camera, which is inefficient and
time-consuming. To meet the requirements of delay intensive and resource hungry
applications in 5G, we propose an edge computing-based photo crowdsourcing
(EC-PCS) framework in this paper. The main objective is to collect a set of
representative photos from ubiquitous mobile and Internet of Things (IoT)
devices at the network edge for real-time 3D model reconstruction, with network
resource and monetary cost considerations. Specifically, we first propose a
photo pricing mechanism by jointly considering their freshness, resolution and
data size. Then, we design a novel photo selection scheme to dynamically select
a set of photos with the required target coverage and the minimum monetary
cost. We prove the NP-hardness of such problem, and develop an efficient
greedy-based approximation algorithm to obtain a near-optimal solution.
Moreover, an optimal network resource allocation scheme is presented, in order
to minimize the maximum uploading delay of the selected photos to the edge
server. Finally, a 3D reconstruction algorithm and a 3D model caching scheme
are performed by the edge server in real time. Extensive experimental results
based on real-world datasets demonstrate the superior performance of our EC-PCS
system over the existing mechanisms.
