3D Reconstruction and Measurement Analysis of a Dense Point Cloud Fused with a Depth Image

Abstract

To solve the problems of holes, noise, and texture information missing in the traditional incremental reconstruction of complex surface objects, a 3D reconstruction method of depth image fusion surface dense point clouds is proposed, and texture feature creation is combined to obtain a 3D reconstruction model that takes into account the main body and details of the reconstructed object. First, the mechanism of surface dense reconstruction based on the patch-based multiview stereo (PMVS) algorithm is analyzed. Combined with the principle of view angle selection of stereo images, surface point cloud density reconstruction is performed. Then, the depth value is optimized by the region growing method, and the optimization model is established. The depth image is fused into a dense surface, and the reconstructed part is supplemented by the depth information. Finally, the Markov random field (MRF) is introduced to describe the richness of image details, and combined with the calculating method of the area coordinate, the texture coordinates are accurately calculated to reproduce the texture details of the 3D reconstruction model. 3D reconstruction experiments are performed on multiple indoor and outdoor model surfaces, and the experimental results show that the proposed method can achieve complete and accurate reconstruction of complex surface objects. Our method provides technical support for complex surface topography detection and has industrial practical significance.