Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
ACM Transactions on Graphics (TOG)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A method for fast search of variable regions on dynamic 3D point clouds
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A method for fast search of variable regions on dynamic 3D point clouds
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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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.