Visual Three-Dimensional Reconstruction Based on Spatiotemporal Analysis Method

To accurately reconstruct the three-dimensional (3D) surface of dynamic objects, we proposed a wrapped phase extraction method for spatiotemporal analysis based on 3D wavelet transform (WT). Our proposed method uses a 2D spatial fringe image combined with the time dimension and forms a 3D image sequence. The encoded fringe image sequence's wrapped phase information was extracted by 3D WT and complex Morlet wavelet, and we improved the wrapped phase extraction's accuracy by using the characteristics of spatiotemporal analysis and a multi-scale analysis of 3D WT, then we reconstructed the measured object by wrapped phase unwrapping and phase height transformation. Our simulation experiment results show that our proposed method can further filter the noise in the time dimension, and its accuracy is better than that of the one- (1D) and two-dimensional (2D) WT wrapped phase extraction method and the 3D Fourier transform wrapped phase extraction method because the reconstructed spherical crown's RMSE value does not exceed 0.25 and the PVE value is less than 0.95. Our results show that the proposed method can be applied to the dynamic 3D reconstruction of a real human thoracic and abdominal surface, which fluctuates slowly with respiration movement, further verifying its effectiveness.

Automated summary

In order to accurately reconstruct the three-dimensional surface of dynamic objects, a wrapped phase extraction method based on 3D wavelet transform (WT) for spatiotemporal analysis was proposed. The method combines a 2D spatial fringe image with the time dimension to form a 3D image sequence. By utilizing 3D WT and complex Morlet wavelet, the wrapped phase information of the encoded fringe image sequence was extracted, and the accuracy of wrapped phase extraction was improved through spatiotemporal analysis and multi-scale analysis of 3D WT. The measured object was then reconstructed through wrapped phase unwrapping and phase height transformation. Simulation experiment results showed that the proposed method can effectively filter noise in the time dimension and achieve higher accuracy compared to other methods.