基于像素插值的改进PMVS稠密重建方法
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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.
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.
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.
引文
1秦红星,胡勇强.基于区域生长的稠密三维点云重建方法.计算机工程与设计,2016,37(2):465-469.
2Furukawa Y,Ponce J.Accurate,dense,and robust multiview stereopsis.IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.[doi:10.1109/TPAMI.2009.161]
3Patch-based multi-view stereo software(PMVA-2).https://wenku.baidu.com/view/f9a27eee9b6648d7c1c746f9.h tml,2009.
4Furukawa Y,Ponce J.Patch-based multi-view stereo software(PMVS-Version 2).http://www.di.ens.fr/pmvs/,2018-08-17.
5王刘正.稠密点云生成算法的研究[硕士学位论文].沈阳:辽宁大学,2016.
6陈攀.多视图三维重建及其评估算法的研究[硕士学位论文].武汉:华中师范大学,2016.
7陈冉.基于准稠稠密匹配配方法的的PMVS算算法改进[硕士学位论文].北京:北京工业大学,2014.
8Lowe DG.Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision,2004,60(2):91-110.[doi:10.1023/B:VISI.0000029664.99615.94]
9肖庆汇.基于序列图像稠密匹配的三维重建[硕士学位论文].南昌:南昌航空大学,2011.
10史颖,王文剑,白雪飞.多特征三维稠密重建方法.计算机科学与探索,2015,9(5):594-603.
2Furukawa Y,Ponce J.Accurate,dense,and robust multiview stereopsis.IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(8):1362-1376.[doi:10.1109/TPAMI.2009.161]
3Patch-based multi-view stereo software(PMVA-2).https://wenku.baidu.com/view/f9a27eee9b6648d7c1c746f9.h tml,2009.
4Furukawa Y,Ponce J.Patch-based multi-view stereo software(PMVS-Version 2).http://www.di.ens.fr/pmvs/,2018-08-17.
5王刘正.稠密点云生成算法的研究[硕士学位论文].沈阳:辽宁大学,2016.
6陈攀.多视图三维重建及其评估算法的研究[硕士学位论文].武汉:华中师范大学,2016.
7陈冉.基于准稠稠密匹配配方法的的PMVS算算法改进[硕士学位论文].北京:北京工业大学,2014.
8Lowe DG.Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision,2004,60(2):91-110.[doi:10.1023/B:VISI.0000029664.99615.94]
9肖庆汇.基于序列图像稠密匹配的三维重建[硕士学位论文].南昌:南昌航空大学,2011.
10史颖,王文剑,白雪飞.多特征三维稠密重建方法.计算机科学与探索,2015,9(5):594-603.