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A model based structural recognition approach is used for 3D detection and localization of vehicles. It is theoretically founded by syntactic pattern recognition using coordinate grammars and depicted by production nets. The computational effort significantly depends on certain tolerance parameters and the distribution of input data in the attribute domain. A brief theoretical survey of these interrelations is accompanied by comparing the performance on synthetic random data to the performance on data from different natural environments.

Assessing the Computational Effort for Structural 3D Vehicle Recognition