Spectral Analysis Driven Sparse Matching of 3D Shapes

Stochastic Matching, which is based on stochastic search; Error-driven Matching, which uses local matching er- rors to drive the solution to an optimal match; Greedy Matching,

The topology of a graph structure does not change under projection: in this way we solve the point correspondence problem by a subgraph matching algorithm between the detected 2D

This paper describes an algorithm for multi-feature match- ing of 3D shapes with priority-driven search. The main con- tribution is an algorithm for searching a database for the

Similar to the recently presented Fast Lattice Shape Matching (FLSM), we compute the position of simulation nodes by convolution of rigid shape matching operators on many

It is com- posed of a meaningful new mesh segmentation algorithm along with a graph matching between the graphs of the query object and each of the graphs that correspond to the

3D Model Reconstruction, for the production of sparse or dense point clouds; 3D Motion Matching, for the automatic reconstruction of virtual reality models from video sequences and

Tree matching can be modeled as a bipartite graph matching problem, where G is a graph, with two disjoint node sets, P and Q, corresponding to the branch sets A and B of the source

Evaluation of the motion flow reconstruction When testing the motion flow on these datasets, we obtain a satisfying motion field due to the regularization step, where each surface