High-accuracy digital volume correlation-based point cloud registration for 3D reconstruction

Three-dimensional (3D) reconstruction plays an important role in intelligent manufacturing, industrial inspection and reverse modeling. The model accuracy of 3D reconstruction has important influence on the final product quality and reliability, and point cloud registration is the key to 3D reconstruction, whose registration accuracy directly affects the final 3D reconstruction accuracy. There are many researches on point cloud registration algorithms, but the existing point cloud methods often have the disadvantages of low accuracy and slow speed when registering large point clouds. To meet this challenge, it is proposed that a high-accuracy point cloud registration method by digital volume correlation (DVC). Firstly, source point cloud and target point cloud are down sampled by voxel grid filter. Subsequently, the intrinsic shape signatures (ISS) feature is used to extract the feature points and random sample consensus (RANSAC) algorithm is used for coarse registration with ISS feature points. Finally, the point clouds are converted into voxels with gray-value information, which will be used for DVC calculation to obtain higher accuracy point cloud registration results. Experimental results show that our method can achieve high precision registration of large point clouds and ensure sufficient registration speed.