Morphable Surface Models

作者:Christian R. Shelton

摘要

We describe a novel automatic technique for finding a dense correspondence between a pair of n-dimensional surfaces with arbitrary topologies. This method employs a different formulation than previous correspondence algorithms (such as optical flow) and includes images as a special case. We use this correspondence algorithm to build Morphable Surface Models (an extension of Morphable Models) from examples. We present a method for matching the model to new surfaces and demonstrate their use for analysis, synthesis, and clustering.

论文关键词:computer vision, learning, correspondence, morphable models, surface matching

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论文官网地址:https://doi.org/10.1023/A:1008170818506