Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2005 Papers
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Exploiting model similarity for indexing and matching to a large model database
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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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.