10. The method as set forth in claim 9 , further comprising acts of: performing, in a search module, a local search for objects using cues from the visual attention cueing block and the geometric grammar block to generate candidate regions of interest (ROIs); and implementing, in a statistical classifier block and within the candidate ROIs, multiple machine learning based object classifiers using, geometric features generated by the implicit geometry engine; and classifying the 3D object of interest as a particular object upon an output of the object classifiers reaching a predetermined threshold.
10. The method as set forth in claim 9 , further comprising acts of: performing, in a search module, a local search for objects using cues from the visual attention cueing block and the geometric grammar block to generate candidate regions of interest (ROIs); and implementing, in a statistical classifier block and within the candidate ROIs, multiple machine learning based object classifiers using, geometric features generated by the implicit geometry engine; and classifying the 3D object of interest as a particular object upon an output of the object classifiers reaching a predetermined threshold. 11. The method as set forth in claim 10 , further comprising an act of receiving, in the implicit geometry engine, the 3D colorized point cloud data and processing the 3D colorized point cloud data along, separate 2D and 3D pathways, such that in the 3D pathway, the 3D colorized point cloud data is converted into implicit representations, while in the 2D pathway, color information in the 3D colorized cloud point, data is projected into 2D representations for cueing and recognition of potential objects.