UNSUPERVISED SEGMENTATION OF DEFORMING 3D MESHES VIA DEFORMATION-AWARE GRAPH CUTS
Keywords:
Graph cut, 3D mesh segmentation, Unsupervised learningAbstract
We propose an unsupervised method for segmenting deforming 3D mesh sequences using a deformation-aware graph cut. Our approach constructs a spatiotemporal graph and formulates segmentation as a minimum s-t cut problem. Unary costs, derived from per-vertex deformation energy, separate near-rigid parts from deforming regions, while pairwise costs enforce spatial smoothness. By iteratively applying max-flow/min-cut, the algorithm greedily extracts coherent parts without supervision. Results show the automatic partitioning of complex animations into meaningful, temporally-consistent components, validating our approach.References
[1] Katz S, Tal A. Hierarchical Mesh Decomposition Using Fuzzy Clustering and Cuts. ACM Transactions on Graphics (Proc. SIGGRAPH), 2003, 22(3): 954–961.
[2] Golovinskiy A, Funkhouser T. Randomized Cuts for 3D Mesh Analysis. ACM Transactions on Graphics, 2008, 27(5): 145.
[3] Kalogerakis E, Hertzmann A, Singh K. Learning 3D Mesh Segmentation and Labeling. ACM Transactions on Graphics, 2010, 29(4): 102.
[4] Lee TY, Wang YS, Chen TG. Segmenting a Deforming Mesh into Near-Rigid Components. The Visual Computer, 2006, 22(9-11): 729–739.
[5] Shapira L, Shamir A, Cohen-Or D. Consistent Mesh Partitioning and Skeletonisation using the Shape Diameter Function. The Visual Computer, 2008, 24(4): 249–259.
[6] Liu R, Zhang H. Segmentation of 3D Meshes through Spectral Clustering. Proceedings of Pacific Graphics, 2004: 298–305.
[7] Boykov Y, Jolly MP. Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. Proceedings of ICCV, 2001: 105–112.
[8] Boykov Y, Kolmogorov V. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1124–1137.