An improved 3D human model reconstruction technique based on Cascade MVSNet

Abstract

In order to improve the capability of feature point extraction and matching in 3D human model reconstruction and enhance the accuracy of it, this paper improves a multi-view-based 3D reconstruction method of the human body model. We first take dozens of photos of a person at the angle of a circle around him, and then perform instance segmentation on the images to remove the background. Next, we introduce Laplace operator in extracting image feature points to sharpen the edges of the images and use KNN algorithm to match feature points on the images. When filtering the matching points, we introduce the Adaptive Locally-Affine Matching (AdaLAM) algorithm to optimize the matching results. Finally, we use incremental SFM and Cascade MVSNet to reconstruct sparse and dense point clouds, respectively, and obtain better reconstruction results. The results show that, compared with the original 3D reconstruction algorithm, the improved method in this paper effectively improves the capability of feature point extraction and matching, enhances the accuracy of the generated human dense point clouds. As a result, the number of point clouds obtained increased by 17.8%.