Research on 3D reconstruction method of cattle face based on image

被引:1
作者
Weng, Zhi [1 ,2 ,3 ]
He, Dongchang [1 ]
Zheng, Yan [1 ]
Zheng, Zhiqiang [1 ,2 ]
Zhang, Yong [3 ]
Gong, Caili [1 ,3 ]
机构
[1] Inner Mongolia Univ, Coll Elect Informat Engn, Hohhot 010018, Peoples R China
[2] Inner Mongolia Univ, State Key Lab Reprod Regulat & Breeding Grassland, Hohhot, Peoples R China
[3] Inner Mongolia Agr Univ, Coll Mech & Elect Engn, Hohhot, Peoples R China
基金
中国国家自然科学基金;
关键词
3D Reconstruction; approximate rigidity; multi-perspective; surface reconstruction; SHAPE;
D O I
10.3233/JIFS-224260
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features.
引用
收藏
页码:10551 / 10563
页数:13
相关论文
共 31 条
[1]  
Biggs B., 2017, CREATURES GREAT SMAL
[2]  
Bregler C, 2000, PROC CVPR IEEE, P690, DOI 10.1109/CVPR.2000.854941
[3]   What Shape Are Dolphins? Building 3D Morphable Models from 2D Images [J].
Cashman, Thomas J. ;
Fitzgibbon, Andrew W. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (01) :232-244
[4]   Computing three dimensional project invariants from a pair of images using the Grassmann-Cayley algebra [J].
Csurka, G ;
Faugeras, O .
IMAGE AND VISION COMPUTING, 1998, 16 (01) :3-12
[5]  
Deguchi K, 2000, IEICE T INF SYST, VE83D, P1408
[6]   Continuous body 3-D reconstruction of limbless animals [J].
Fu, Qiyuan ;
Mitchel, Thomas W. ;
Kim, Jin Seob ;
Chirikjian, Gregory S. ;
Li, Chen .
JOURNAL OF EXPERIMENTAL BIOLOGY, 2021, 224 (06)
[7]   Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images [J].
Gabeur, Valentin ;
Franco, Jean-Sebastien ;
Martin, Xavier ;
Schmid, Cordelia ;
Rogez, Gregory .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, :2232-2241
[8]   Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds From Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction [J].
Han, Zhizhong ;
Wang, Xiyang ;
Liu, Yu-Shen ;
Zwicker, Matthias .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, :10441-10450
[9]  
Jialu J., 2020, COMPUTER APPL SOFTWA, V37, P127
[10]   Pseudo RGB-D Face Recognition [J].
Jin, Bo ;
Cruz, Leandro ;
Goncalves, Nuno .
IEEE SENSORS JOURNAL, 2022, 22 (22) :21780-21794