Fast and accurate point cloud registration by exploiting inverse cumulative histograms (ICHs)

The automatic and accurate alignment of captured point clouds is an important task for digitization, reconstruction and interpretation of 3D scenes. Standard approaches such as the ICP algorithm and Least Squares 3D Surface Matching require a good a priori alignment of the scans for obtaining satisfactory results. In this paper, we propose a new and fast methodology for automatic point cloud registration which does not require a good a priori alignment and is still able to recover the transformation parameters between two point clouds very accurately. The registration process is divided into coarse registration based on 3D/2D correspondences and fine registration exploiting 3D/3D correspondences. As the reliability of single 3D/2D correspondences is directly taken into account by applying Inverse Cumulative Histograms (ICHs), this approach is also capable to detect reliable tie points, even when using noisy raw point cloud data. The performance of the proposed methodology is demonstrated on a benchmark dataset and therefore allows for direct comparison with other already existing or future approaches.