3D object recognition by geometric context and Gaussian-Mixture-Model-based plane classification

In this paper, we propose a new 3D object recognition method. The method segments a 3D point set into a number of planar patches and extracts the Inter-Plane Relationships (IPRs) for all patches. Based on the IPRs, the method determines the High Level Feature (HLF) for each patch. A Gaussian-Mixture-Model-based plane classifier is then employed to classify each patch into one belonging to a certain model object. Finally, a recursive plane clustering procedure is performed to cluster the classified planes into the model objects. Experimental results demonstrate that the proposed method has high success rates in object recognition with real-world data. Also, the method can be implemented for real-time operation.