Identification of highly similar 3d objects using model saliency

We present a novel approach for identifying 3D objects from a database of models, highly similar in shape, using range data acquired in unconstrained settings from a limited number of viewing directions. We are addressing also the challenging case of identifying targets not present in the database. The method is based on learning offline saliency tests for each object in the database, by maximizing an objective measure of discriminability with respect to other similar models. Our notion of model saliency differs from traditionally used structural saliency that characterizes weakly the uniqueness of a region by the amount of 3D texture available, by directly linking discriminability with the Bhattacharyya distance between the distribution of errors between the target and its corresponding ground truth, respectively other similar models. Our approach was evaluated on thousands of queries obtained by different sensors and acquired in various operating conditions and using a database of hundreds of models. The results presented show a significant improvement in the recognition performance when using saliency compared to global point-to-point mismatch errors, traditionally used in matching and verification algorithms.