Abstract
One of the most usual strategies for tackling the 3D object recognition problem consists of representing the objects by their appearance. 3D recognition can therefore be converted into a 2D shape recognition matter. This paper is focused on carrying out an in depth qualitative and quantitative analysis with regard to the performance of 2D shape recognition methods when they are used to solve 3D object recognition problems. Well known shape descriptors (contour and regions) and 2D similarities measurements (deterministic and stochastic) are thus combined to evaluate a wide range of solutions. In order to quantify the efficiency of each approach we propose three parameters: Hard Recognition Rate ( Hr), Weak Recognition Rate ( Wr) and Ambiguous Recognition Rate ( Ar). These parameters therefore open the evaluation to active recognition methods which deal with uncertainty. Up to 42 combined methods have been tested on two different experimental platforms using public database models. A detailed report of the results and a discussion, including detailed remarks and recommendations, are presented at the end of the paper.
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