Cluster-Based Point Cloud Analysis for Rapid Scene Interpretation

A histogram-based method for the interpretation of three-dimensional (3D) point clouds is introduced, where point clouds represent the surface of a scene of multiple objects and background. The proposed approach relies on a pose-invariant object representation that describes the distribution of surface point-pair relations as a model histogram. The models of the used objects are previously trained and stored in a database. The paper introduces an algorithm that divides a large number of randomly drawn surface points, into sets of potential candidates for each object model. Then clusters are established in every model-specific point set. Each cluster contains a local subset of points, which is evaluated in six refinement steps. In the refinement steps point-pairs are built and the distribution of their relationships is used to select and merge reliable clusters or to delete them in the case of uncertainty. In the end, the algorithm provides local subsets of surface points, labeled as an object. In the experimental section the approach shows the capability for scene interpretation in terms of high classification rates and fast processing times for both synthetic and real data.

[1]  Mohamed Daoudi,et al.  A practical approach for 3D model indexing by combining local and global invariants , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[2]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[3]  G. Hirzinger,et al.  A Novel System Approach to Multisensory Data Acquisition , 2004 .

[4]  Eric Wahl,et al.  A Method for Fast Search of Variable Regions on Dynamic 3D Point Clouds , 2005, DAGM-Symposium.

[5]  Eric Wahl,et al.  Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[6]  Aly A. Farag,et al.  Surfacing Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.