A unified framework for automated registration of point clouds, mesh surfaces and <scp>3D</scp> models by using planar surfaces
Yuan Zhao, Hang Zhao, Marko Radanović, Kourosh Khoshelham
Abstract
Abstract Registration of 3D spatial data and models is a fundamental task in applications such as mapping, positioning and virtual/augmented reality. Most of the existing 3D registration methods such as iterative closest point (ICP) and recent learning‐based methods are dedicated to point cloud registration, and rely heavily on point‐wise correspondences, which limits their ability to address registration problems across different data types. Since man‐made objects and buildings usually contain many planar surfaces, it is possible to use the planes for accurate registration of different data and models. In this paper, a unified registration framework is proposed consisting of a plane extraction module, which can extract planes from various forms of spatial data such as point clouds or surface‐based 3D models, and a registration module, which performs automatic registration based on the extracted planes. Tests show that the proposed method can handle small‐overlap registration across all these data types with high success rates. The result of point cloud registration also indicates that the method achieves better accuracy as compared to ICP.