Visual Three-Dimensional Reconstruction Based on Spatiotemporal Analysis Method

Abstract

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.

Three-Dimensional Point Cloud-Filtering Method Based on Image Segmentation and Absolute Phase Recovery

“…The types of noise point clouds produced by 3D reconstruction include scattered noise point clouds, blocky noise point clouds, sudden noise point clouds, etc. [30][31][32]. According to Equation (1), it is concluded that there is a corresponding relationship between the noise points of the 3D point cloud and the point cloud mapping image, and the noise point cloud can be removed by using the corresponding relationship.…”