Surface-based matching of 3D point clouds with variable coordinates in source and target system

The automatic co-registration of point clouds, representing three-dimensional (3D) surfaces, is an important technique in 3D reconstruction and is widely applied in many different disciplines. An alternative approach is proposed here that estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface. The approach uses the nonlinear Gauss–Helmert model, minimizing the quadratically constrained least squares problem. This approach has the ability to match arbitrarily oriented 3D surfaces captured from a number of different sensors, on different time-scales and at different resolutions. In addition to the 3D surface-matching paths, the mathematical model allows the precision of the point clouds to be assessed after adjustment. The error behavior of surfaces can also be investigated based on the proposed approach. Some practical examples are presented and the results are compared with the iterative closest point and the linear least-squares approaches to demonstrate the performance and benefits of the proposed technique.