TY - GEN
T1 - Matching 3D models with shape distributions
AU - Osada, Robert
AU - Funkhouser, Thomas
AU - Chazelle, Bernard
AU - Dobkin, David
PY - 2001
Y1 - 2001
N2 - Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring the global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence or model fitting. We find that the dissimilarities between sampled distributions of simple shape functions (e.g. the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g. cars versus airplanes) in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, reflections, tessellations, simplifications and model degeneracies. They can be evaluated quickly, and thus the proposed method could be applied as a pre-classifier in an object recognition system or in an interactive content-based retrieval application.
AB - Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring the global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is simpler than traditional shape matching methods that require pose registration, feature correspondence or model fitting. We find that the dissimilarities between sampled distributions of simple shape functions (e.g. the distance between two random points on a surface) provide a robust method for discriminating between classes of objects (e.g. cars versus airplanes) in a moderately sized database, despite the presence of arbitrary translations, rotations, scales, reflections, tessellations, simplifications and model degeneracies. They can be evaluated quickly, and thus the proposed method could be applied as a pre-classifier in an object recognition system or in an interactive content-based retrieval application.
UR - http://www.scopus.com/inward/record.url?scp=84884776532&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884776532&partnerID=8YFLogxK
U2 - 10.1109/SMA.2001.923386
DO - 10.1109/SMA.2001.923386
M3 - Conference contribution
AN - SCOPUS:84884776532
SN - 0769508537
SN - 9780769508535
T3 - Proceedings - International Conference on Shape Modeling and Applications, SMI 2001
SP - 154
EP - 166
BT - Proceedings - International Conference on Shape Modeling and Applications, SMI 2001
T2 - 2001 International Conference on Shape Modeling and Applications - Shape Modeling International, SMI 2001
Y2 - 7 May 2001 through 11 May 2001
ER -