TY - GEN
T1 - Monocular reconstruction of non-rigid shapes using optical flow feedback
AU - Liu, Jiaqing
AU - Shen, Xukun
AU - Hu, Yong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In this paper we describe a variational approach to reconstruct the non-rigid shape from a monocular video sequence based on optical flow feedback. To obtain the dense 2D correspondences from the image sequence, which is critical for 3D reconstruction, we formulate the multi-frame optical flow problem as a global energy minimization process using subspace constraints, settles the problems of large displacements and high cost caused by dimensionality elegantly. Using the long-Term trajectory tracked by optical flow field as input, our method estimate the depth of traced pixel in each frame based on the Non-Rigid Structure from Motion(SFM) algorithm. And finally, we refine the 3D shape via interpolation on recovered 3D point cloud and camera parameters. The experiment on real sequence of different objects demonstrates the accuracy and robustness of our framework.
AB - In this paper we describe a variational approach to reconstruct the non-rigid shape from a monocular video sequence based on optical flow feedback. To obtain the dense 2D correspondences from the image sequence, which is critical for 3D reconstruction, we formulate the multi-frame optical flow problem as a global energy minimization process using subspace constraints, settles the problems of large displacements and high cost caused by dimensionality elegantly. Using the long-Term trajectory tracked by optical flow field as input, our method estimate the depth of traced pixel in each frame based on the Non-Rigid Structure from Motion(SFM) algorithm. And finally, we refine the 3D shape via interpolation on recovered 3D point cloud and camera parameters. The experiment on real sequence of different objects demonstrates the accuracy and robustness of our framework.
KW - 3D reconstruction
KW - Muti-frame optical flow
KW - Non-rigid structure from motion
UR - https://www.scopus.com/pages/publications/85067068128
U2 - 10.1109/ICVRV.2017.00014
DO - 10.1109/ICVRV.2017.00014
M3 - 会议稿件
AN - SCOPUS:85067068128
T3 - Proceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017
SP - 24
EP - 29
BT - Proceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Virtual Reality and Visualization, ICVRV 2017
Y2 - 21 October 2017 through 22 October 2017
ER -