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PROJECT TITLE :

An Edge Computing-based Photo Crowdsourcing Framework for Real-time 3D Reconstruction

ABSTRACT:

The process of image-based three-dimensional (3D) reconstruction takes a collection of photographs and uses them to build a 3D model. This technique has a wide range of potential applications, including augmented reality (AR) and disaster recovery, among others. The majority of the currently available strategies for 3D reconstruction call for a mobile user to walk around the target area and reconstruct objectives with a hand-held camera. This is a laborious and inefficient process. In this paper, we propose a framework for edge computing-based photo crowdsourcing (EC-PCS) in order to meet the requirements of delay-intensive and resource-hungry applications in 5G. The primary goal is to collect a set of representative photographs from pervasive mobile and Internet of Things (IoT) devices at the network edge in order to reconstruct a real-time 3D model while taking into consideration the amount of network resources and the cost in monetary terms. To be more specific, we first suggest a pricing mechanism for photos by jointly taking into consideration their level of freshness, resolution, and data size. Then, we come up with an innovative plan for selecting photos in order to dynamically choose a collection of pictures that have the necessary amount of target coverage at the lowest possible cost. We provide proof that such a problem is NP-hard and then design and implement an effective algorithm that is based on greedy behavior to obtain a solution that is nearly optimal. Additionally, an optimal network resource allocation scheme is presented in order to minimize the maximum uploading delay of the chosen photos to the edge server. This is done by using the scheme to allocate network resources in the best possible way. In the end, a real-time 3D reconstruction algorithm and a 3D model caching scheme are both carried out by the edge server. The extensive experimental results that are based on real-world datasets demonstrate that our EC-PCS system performs significantly better than the other mechanisms that are currently in place.


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