Framework for quantitative performance evaluation of shape decomposition algorithms

Despite of intensive research on shape decomposition algorithms, their performance evaluation remains qualitative today. The intention of this work is to close this gap by proposing a general framework for quantitative performance evaluation of shape decomposition algorithms. The proposed framework is of supervised nature and based on a benchmark database from a large-scale psychological study with manually specified ground truth. We discuss various variants of dissimilarity functions for comparing two decompositions. A preliminary comparison study using five shape decomposition methods and an ensemble technique demonstrates the usefulness of our approach. In particular, the quantitative results well coincide with visual comparison of decompositions.