National University of Defense Technology, Changsha.
IEEE Trans Vis Comput Graph. 2013 Nov;19(11):1885-94. doi: 10.1109/TVCG.2013.15.
Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.
特征匹配是众多计算机图形学和计算机视觉应用的核心难题。我们提出了 SuperMatching 算法，用于在两组特征之间寻找对应关系。它通过考虑三点或更高阶点元组来实现这一点，超越了通常使用的逐点和成对方法。SuperMatching 使用表示相似性度量的超对称张量进行公式化，该张量考虑了特征之间的特征相似性和几何约束：特征匹配被表述为高阶图匹配问题。SuperMatching 利用超对称性来设计一种有效的采样策略来估计相似性张量，并紧凑地存储估计的张量。匹配是通过一种高效的高阶幂迭代方法来完成的，该方法利用了这种紧凑的表示。在合成和真实数据上的实验表明，SuperMatching 比其他最先进的方法提供了更准确的特征匹配，适用于广泛的 2D 和 3D 特征，具有竞争力的计算成本。
