Image-Based 3D Reconstruction on Distributed Hash Network

Image-Based 3D Reconstruction on Distributed Hash Network

Jin Hua Zhong (Hubei University of Technology, Wuhan, China) and Wan Fang (Hubei University of Technology, Wuhan, China)
DOI: 10.4018/IJMCMC.2018100104

Abstract

In order to optimize the workflow of iterative 3D reconstruction and support the goal of massive image data processing, high performance and high scalability, this article proposes an image distributed computing framework FIODH. It is a distributed computing framework based on distributed hash algorithm, which accomplishes the task of storing, calculating and merging the image data in multiple nodes. A SIFT algorithm is used to extract feature points from the original images which are distributed on the hash nodes. During the process of image clustering computation, the agent nodes are responsible for task management and intermediate result calculation. The clustering results in hierarchical trees which can be converted into computational tasks and assigned to the appropriate nodes. The experimental analysis shows that the algorithm has achieved satisfactory results in efficiency and error adjustment. In a large amount of experiment data, the advantage of the algorithm is more obvious.
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Introduction

Background

The estimation of camera pose and the generation from the sparse points, the basis for dense matching and texture mapping, are very important in 3D reconstruction. There are two main methods, one is the overall reconstruction, and the other is iterative reconstruction. The overall reconstruction processes all the images together and avoids the problem of local optimization, however its calculation is very large and once a new picture is added, all the images need to be reprocessed.

While iterative reconstruction uses the relationship between multiple images and perform incremental computation. When a new image is added, only this new one is required to be calculated. The disadvantage of iterative reconstruction is the cumulative error caused by the calculation of the image based on the specified sequence. In addition, much of time is consumed in the process of image matching. So when reconstructing from large-scale images, it often causes slow operation and even failure of reconstruction.

Therefore, the availability and capacity of the image storage system have become the two important issues in the calculation. The real availability and good performance of the automatic elastic telescopic storage system can not only solve the problems in storage system, like machine failure, hard disk damage and user data overflow, but also reduce the risk of workload, and ensure system be more durable and stable.

So, in this paper, we focus on how to introduce the high-performance storage and calculation in the iterative reconstruction.

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