3D model retrieval based on color + geometry signatures

Color plays a significant role in the recognition of 3D objects and scenes from the perspective of cognitive psychology. In this paper, we propose a new 3D model retrieval method, focusing on not only the geometric features but also the color features of 3D mesh models. Firstly, we propose a new sampling method that samples the models in the regions of either geometry-high-variation or color-high-variation. After collecting geometry + color sensitive sampling points, we cluster them into several classes by using a modified ISODATA algorithm. Then we calculate the feature histogram of each model in the database using these clustered sampling points. For model retrieval, we compare the histogram of an input model to the stored histograms in the database to find out the most similar models. To evaluate the retrieval method based on the new color + geometry signatures, we use the precision/recall performance metric to compare our method with several classical methods. Experiment results show that color information does help improve the accuracy of 3D model retrieval, which is consistent with the postulate in psychophysics that color should strongly influence the recognition of objects.