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:2019,28(7):157-161
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基于像素插值的改进PMVS稠密重建方法
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.陆军炮兵防空兵学院 士官学校, 沈阳 110867)
Dense Reconstruction Method Based on Improved PMVS and Pixel Interpolation
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.NCO School, Artillery and Air-defense Forces Academy of Army, Shenyang 110867, China)
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投稿时间:2019-01-07    修订日期:2019-02-03
中文摘要: 针对纹理稀疏区域重建效果差的问题,本文提出一种基于像素插值的改进稠密重建算法.基于面片的多视图稠密重建方法(PMVS)能够自动忽略外部点和障碍点,相比较于其他三维稠密重建算法该算法更准确,简单,高效.但是在纹理稀疏的区域会出现孔洞残缺等问题,且现有的匹配候选点选取策略会使得局部细节失真边缘残缺.本文针对这些问题提出了一种基于像素插值的特征点选取方法,增加纹理稀疏区域的特征点,使特征点分布均匀,提出一种更合理的候选点选取策略,减少错误匹配.实验表明本文提出的方法不仅能保证纹理稀疏区域的重建效果,还能有效剔除误匹配点,提高重建精度.
Abstract:The Patch-based Multi-View Stereopsis (PMVS) dense reconstruction method can automatically ignore external points and obstacle points. Compared with other 3D dense reconstruction algorithms, the algorithm is more accurate, simple, and efficient. However, holes appear in areas with sparse textures, and existing candidate points selection strategies may cause edge defects and local detail distortion. Aiming to solve these problems, this study proposes a method of pixel interpolation feature point selection based on Scale Invariant Feature Transform (SIFT), which increases the feature points of texture sparse regions and makes the feature points be evenly distributed. A more reasonable candidate point selection strategy is proposed to reduce false matching. Experiments show that the proposed method can not only ensure the reconstruction effect of sparse texture regions, but also effectively eliminate mismatched points and improve reconstruction accuracy.
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隗娜,郭向坤,董志勇,张兆伟.基于像素插值的改进PMVS稠密重建方法.计算机系统应用,2019,28(7):157-161
WEI Na,GUO Xiang-Kun,DONG Zhi-Yong,ZHANG Zhao-Wei.Dense Reconstruction Method Based on Improved PMVS and Pixel Interpolation.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):157-161

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