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Maximum spatial-temporal isometric cluster for dynamic surface correspondence
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DOI：10.1007/s00371-019-01655-0.png)
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Isometric correspondence is an important technique for surface correspondence. Recently, numerous algorithms have been proposed to build isometric mapping. However, those methods tend to be error prone due to the topological variation and noises of the dynamic surface. To address this issue, we propose a dynamic surface correspondence method by computing maximum spatial-temporal isometric cluster. Firstly, the algorithm defines a maximum isometric cluster score to measure the correspondence quality of each cluster in the product space. Then, the maximum problem is formulated into a quadratic programming problem. Furthermore, we define a similarity function which explicitly encodes the spatial-temporal consistence of the dynamic surface. It can greatly reduce the solving dimension, and improve the correspondence accuracy. Finally, the result is extended to the dense correspondence by a geodesic distance vector. Experimental results show that our algorithm can generate consistent correspondence on three databases of surface sequences, which outperforms existing state-of-the-art algorithms.
Keyword:
Dynamic surface correspondence
Maximum spatial-temporal isometric cluster
Quadratic programming problem
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