TY - GEN
T1 - Skeleton-based 3D model descriptor and its application in non-rigid shape retrieval
AU - Zhu, Yiran
AU - Kang, Jiaqi
AU - Lv, Chenlei
AU - Xu, Shu
AU - Xue, Yanping
AU - Zhang, Dan
AU - Wang, Xingce
AU - Wu, Zhongke
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In this paper, we propose a novel skeleton-based 3D model descriptor to describe the shape of a triangular surface mesh. The main idea is to match skeleton tree by comparing the skeleton tree structure with shape surface area distribution, which is achieved in three steps as follows. First, based on the extracted skeletons of a non-rigid shape and geodesic distance computation, the center point in skeleton is defined and detected. Second, a skeleton tree is constructed based on the relationship between the center point and other discrete points in the skeleton. Finally, a correspondence between the skeleton tree and the area distribution of the non-rigid shape surface is established, and the skeleton-based descriptor is obtained. The advantages of our descriptor are three-fold: (1) scale invariance of geodesic distance and area distribution, (2) low computational complexity of geodesic distance, and (3) automatic topology repair. Experimental results demonstrate effectiveness and accuracy of our method, which is applicable to non-rigid shape comparison and retrieval.
AB - In this paper, we propose a novel skeleton-based 3D model descriptor to describe the shape of a triangular surface mesh. The main idea is to match skeleton tree by comparing the skeleton tree structure with shape surface area distribution, which is achieved in three steps as follows. First, based on the extracted skeletons of a non-rigid shape and geodesic distance computation, the center point in skeleton is defined and detected. Second, a skeleton tree is constructed based on the relationship between the center point and other discrete points in the skeleton. Finally, a correspondence between the skeleton tree and the area distribution of the non-rigid shape surface is established, and the skeleton-based descriptor is obtained. The advantages of our descriptor are three-fold: (1) scale invariance of geodesic distance and area distribution, (2) low computational complexity of geodesic distance, and (3) automatic topology repair. Experimental results demonstrate effectiveness and accuracy of our method, which is applicable to non-rigid shape comparison and retrieval.
KW - Area distribution
KW - Geodesic distance
KW - Non-rigid shape retrieval
KW - Skeleton tree
UR - http://www.scopus.com/inward/record.url?scp=85094322105&partnerID=8YFLogxK
U2 - 10.1109/ICVRV47840.2019.00017
DO - 10.1109/ICVRV47840.2019.00017
M3 - Conference article published in proceeding or book
AN - SCOPUS:85094322105
T3 - Proceedings - 2019 International Conference on Virtual Reality and Visualization, ICVRV 2019
SP - 50
EP - 58
BT - Proceedings - 2019 International Conference on Virtual Reality and Visualization, ICVRV 2019
A2 - Wang, Dangxiao
A2 - Cadavid, Andres Navarro
A2 - Liu, Yue
A2 - Xu, Mingliang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Virtual Reality and Visualization, ICVRV 2019
Y2 - 21 November 2019 through 22 November 2019
ER -