TY - GEN
T1 - Heat-mapping
T2 - A robust approach toward perceptually consistent mesh segmentation
AU - Fang, Yi
AU - Sun, Mengtian
AU - Kim, Minhyong
AU - Ramani, Karthik
PY - 2011
Y1 - 2011
N2 - 3D mesh segmentation is a fundamental low-level task with applications in areas as diverse as computer vision, computer-aided design, bio-informatics, and 3D medical imaging. A perceptually consistent mesh segmentation (PCMS), as defined in this paper is one that satisfies 1) in-variance to isometric transformation of the underlying surface, 2) robust to the perturbations of the surface, 3) robustness to numerical noise on the surface, and 4) close conformation to human perception. We exploit the intelligence of the heat as a global structure-aware message on a meshed surface and develop a robust PCMS scheme, called Heat-Mapping based on the heat kernel. There are three main steps in Heat-Mapping. First, the number of the segments is estimated based on the analysis of the behavior of the Laplacian spectrum. Second, the heat center, which is defined as the most representative vertex on each segment, is discovered by a proposed heat center hunting algorithm. Third, a heat center driven segmentation scheme reveals the PCMS with a high consistency towards human perception. Extensive experimental results on various types of models verify the performance of Heat-Mapping with respect to the consistent segmentation of articulated bodies, the topological changes, and various levels of numerical noise.
AB - 3D mesh segmentation is a fundamental low-level task with applications in areas as diverse as computer vision, computer-aided design, bio-informatics, and 3D medical imaging. A perceptually consistent mesh segmentation (PCMS), as defined in this paper is one that satisfies 1) in-variance to isometric transformation of the underlying surface, 2) robust to the perturbations of the surface, 3) robustness to numerical noise on the surface, and 4) close conformation to human perception. We exploit the intelligence of the heat as a global structure-aware message on a meshed surface and develop a robust PCMS scheme, called Heat-Mapping based on the heat kernel. There are three main steps in Heat-Mapping. First, the number of the segments is estimated based on the analysis of the behavior of the Laplacian spectrum. Second, the heat center, which is defined as the most representative vertex on each segment, is discovered by a proposed heat center hunting algorithm. Third, a heat center driven segmentation scheme reveals the PCMS with a high consistency towards human perception. Extensive experimental results on various types of models verify the performance of Heat-Mapping with respect to the consistent segmentation of articulated bodies, the topological changes, and various levels of numerical noise.
UR - http://www.scopus.com/inward/record.url?scp=79955754307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955754307&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2011.5995695
DO - 10.1109/CVPR.2011.5995695
M3 - Conference contribution
AN - SCOPUS:79955754307
SN - 9781457703942
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 2145
EP - 2152
BT - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PB - IEEE Computer Society
ER -