TY - JOUR
T1 - 3D reconstruction of human head based on consumer RGB-D sensors
AU - Liu, Zihan
AU - Gong, Guanghong
AU - Li, Ni
AU - Yu, Zihao
N1 - Publisher Copyright:
© 2020 World Scientific Publishing Company.
PY - 2020/12
Y1 - 2020/12
N2 - Three-dimensional (3D) reconstruction of a human head with high precision has promising applications in scientific research, product design and other fields. However, it still faces resistance from two factors. One is inaccurate registration caused by symmetrical distribution of head feature points, and the other is economic burden due to high-accuracy sensors. Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years. Based on our multi-Kinect system, a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper. A registration method for multi-source point clouds is provided, which can reduce the fusion differences and reconstruct the head model accurately. In addition, a template-based texture generation algorithm is presented to generate a fine texture. The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect.
AB - Three-dimensional (3D) reconstruction of a human head with high precision has promising applications in scientific research, product design and other fields. However, it still faces resistance from two factors. One is inaccurate registration caused by symmetrical distribution of head feature points, and the other is economic burden due to high-accuracy sensors. Research on 3D reconstruction with portable consumer RGB-D sensors such as the Microsoft Kinect has been highlighted in recent years. Based on our multi-Kinect system, a precise and low-cost three-dimensional modeling method and its system implementation are introduced in this paper. A registration method for multi-source point clouds is provided, which can reduce the fusion differences and reconstruct the head model accurately. In addition, a template-based texture generation algorithm is presented to generate a fine texture. The comparison and analysis of our experiments show that our method can reconstruct a head model in an acceptable time with less memory and better effect.
KW - Head reconstruction
KW - closure differences
KW - point cloud registration
KW - texture generation
UR - https://www.scopus.com/pages/publications/85099372435
U2 - 10.1142/S1793962320500610
DO - 10.1142/S1793962320500610
M3 - 文章
AN - SCOPUS:85099372435
SN - 1793-9623
VL - 11
JO - International Journal of Modeling, Simulation, and Scientific Computing
JF - International Journal of Modeling, Simulation, and Scientific Computing
IS - 6
M1 - 2050061
ER -