A Network for structural dense displacement based on 3D deformable mesh model and optical flow
<|reference_start|>A Network for structural dense displacement based on 3D deformable mesh model and optical flow: This study proposes a Network to recognize displacement of a RC frame structure from a video by a monocular camera. The proposed Network consists of two modules which is FlowNet2 and POFRN-Net. FlowNet2 is used to generate dense optical flow as well as POFRN-Net is to extract pose parameter H. FlowNet2 convert two video frames into dense optical flow. POFRN-Net is inputted dense optical flow from FlowNet2 to output the pose parameter H. The displacement of any points of structure can be calculated from parameter H. The Fast Fourier Transform (FFT) is applied to obtain frequency domain signal from corresponding displacement signal. Furthermore, the comparison of the truth displacement on the First floor of the First video is shown in this study. Finally, the predicted displacements on four floors of RC frame structure of given three videos are exhibited in the last of this study.<|reference_end|>
@article{du2024a,
    title={A Network for structural dense displacement based on 3D deformable mesh
  model and optical flow},
    author={Peimian Du, Qicheng Guo, Yanru Li},
    journal={arXiv preprint arXiv:2402.06329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06329},
    primaryClass={cs.CV eess.IV}
}