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基于结构光运动恢复的多视图三维重建技术研究

Research on Multi-View 3D Reconstruction Technology Based on SFM.

作者信息

Gao Lei, Zhao Yingbao, Han Jingchang, Liu Huixian

机构信息

School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

出版信息

Sensors (Basel). 2022 Jun 9;22(12):4366. doi: 10.3390/s22124366.

Abstract

Multi-view 3D reconstruction technology is used to restore a 3D model of practical value or required objects from a group of images. This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point extraction results, increases the number of feature points, adds proportional constraints to improve the robustness of feature-point matching, and uses RANSAC to eliminate false matching. In the sparse reconstruction stage, the traditional incremental SFM algorithm takes a long time, but the accuracy is high; the traditional global SFM algorithm is fast, but its accuracy is low; aiming at the disadvantages of traditional SFM algorithm, this paper proposes a hybrid SFM algorithm, which avoids the problem of the long time consumption of incremental SFM and the problem of the low precision and poor robustness of global SFM; finally, the MVS algorithm of depth-map fusion is used to complete the dense reconstruction of objects, and the related algorithms are used to complete the surface reconstruction, which makes the reconstruction model more realistic.

摘要

多视图三维重建技术用于从一组图像中恢复具有实际价值或所需的物体的三维模型。本文设计并实现了一套多视图三维重建技术,采用SIFT和SURF特征点提取结果的融合方法,增加了特征点数量,添加比例约束以提高特征点匹配的鲁棒性,并使用RANSAC消除错误匹配。在稀疏重建阶段,传统的增量式SFM算法耗时较长,但精度较高;传统的全局SFM算法速度较快,但其精度较低;针对传统SFM算法的缺点,本文提出了一种混合SFM算法,该算法避免了增量式SFM耗时较长的问题以及全局SFM精度低和鲁棒性差的问题;最后,使用深度图融合的MVS算法完成物体的密集重建,并使用相关算法完成表面重建,使重建模型更加逼真。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f163/9230687/e883de0486dc/sensors-22-04366-g001.jpg

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