Automatic 3D Surface Co-Registration Using Keypoint Matching

A framework for co-registering multi-temporal 3D point cloud surfaces (PCSs) is presented, which addresses the co-registration of urban and non-urban 3D surfaces formed by 3D points. These surfaces are acquired from different surface measurement sensors and are in different coordinate systems. No prior information about initial transformation parameters or proximate matching is assumed. A keypoint matching approach is proposed to co-register two PCSs. First, surface curvature information is utilized for scale-invariant keypoint extraction. Then, every keypoint is characterized by a scale, rotation, and translation invariant surface descriptor called the radial geodesic distance-slope histogram. Keypoints with similar surface descriptors on the two PCSs are matched using bipartite graph matching. Given scale, rotation and translation changes between PCS pairs, co-registration tests on multi-sensor urban and non-urban datasets gave rotation errors from 0.017 degrees to 0.023 degrees, translation errors from 0.007 m to 0.013 m and scale factor errors from 0.0002 to 0.0014.