TY - GEN
T1 - Skeleton features distribution for 3D object retrieval
AU - Hayashi, Tomoki
AU - Raynal, Benjamin
AU - Nozick, Vincent
AU - Saito, Hideo
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a 3D shape similarity measurement method using the surface skeleton of a voxelized 3D shape. A set of features are extracted from the skeleton by combining the skeleton and the distance map of the subject. The distribution of these extracted features is used to represent the 3D shape. Two 3D shapes can be compared with a similarity score computed from the distance between their respective features distribution. Our method is robust to noise and to partial occlusion. Moreover, it is scale and rotation invariant. We tested and validated our method with various objects with different shape and topology.
AB - In this paper, we propose a 3D shape similarity measurement method using the surface skeleton of a voxelized 3D shape. A set of features are extracted from the skeleton by combining the skeleton and the distance map of the subject. The distribution of these extracted features is used to represent the 3D shape. Two 3D shapes can be compared with a similarity score computed from the distance between their respective features distribution. Our method is robust to noise and to partial occlusion. Moreover, it is scale and rotation invariant. We tested and validated our method with various objects with different shape and topology.
UR - http://www.scopus.com/inward/record.url?scp=84872582405&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872582405&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84872582405
SN - 9784901122115
T3 - Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
SP - 377
EP - 380
BT - Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011
T2 - 12th IAPR Conference on Machine Vision Applications, MVA 2011
Y2 - 13 June 2011 through 15 June 2011
ER -