Reconstruction of 3D Non-Rigid Moving Human Targets Based on Reliable Estimation of Contour Deformation Degree

The human body is a typical non-rigid object, and its 3D reconstruction is a classic problem in the field of computer vision. Due to the inherent complexity and dynamism of the human body, it is not suitable for existing non-rigid 3D motion reconstruction algorithms that assume that the number of shape bases of non-rigid bodies is known. The number of shape bases is very important for 3D reconstruction methods. If the number of shape bases is estimated incorrectly in contour deformation estimation, it can lead to unreliable or even complete failure of the reconstruction algorithm. Therefore, this paper designs a 3D non-rigid motion human object reconstruction algorithm based on reliable estimation of contour deformation degree. This algorithm leverage Scale Invariant Feature Transform (SIFT) to obtain non-rigid moving human target features. Firstly, the contour appearance model of the moving human sequence is used to extract the contour feature sequence, which is preprocessed based on the contour appearance depth feature; Furthermore, the deformation degree of the contour is reconstructed and the calculation process of the number of shape bases was optimized, which is no longer simply defined as known. This method optimizes and solves the problem of missing data, improves the reliability of estimating the degree of contour deformation, and completes target reconstruction. The experimental results show that the three-dimensional reconstruction algorithm can accurately reconstruct the changes in the movements of athletes’ shots; The accuracy of 3D reconstruction can reach 95.98%; Moreover, PSNR, SSIM, and MSE indexes performed well with smaller fluctuation range, and the distribution of three-dimensional reconstructed scattering points is very close to the three-dimensional position distribution of real scattering points, and the three-dimensional reconstruction effect is good with strong reliability.