TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds
This addresses a challenging mapping problem for computer industry applications involving 3D point clouds, but it appears incremental as it builds on existing Teichmüller theory.
The paper tackles the problem of mapping 3D point clouds by developing TEMPO, a method for computing Teichmüller extremal mappings with uniform conformality distortions, which enables accurate recognition and classification of point clouds.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mappings, which guarantee uniform conformality distortions, on point cloud surfaces. Based on the discrete analogue, we propose a novel method called TEMPO for computing Teichmüller extremal mappings between feature-endowed point clouds. Using our proposed method, the Teichmüller metric is introduced for evaluating the dissimilarity of point clouds. Consequently, our algorithm enables accurate recognition and classification of point clouds. Experimental results demonstrate the effectiveness of our proposed method.