We present an accurate stereo matching method using local expansion moves
based on graph cuts. This new move-making scheme is used to efficiently infer
per-pixel 3D plane labels on a pairwise Markov random field (MRF) that
effectively combines recently proposed slanted patch matching and curvature
regularization terms. The local expansion moves are presented as many
alpha-expansions defined for small grid regions. The local expansion moves
extend traditional expansion moves by two ways: localization and spatial
propagation. By localization, we use different candidate alpha-labels according
to the locations of local alpha-expansions. By spatial propagation, we design
our local alpha-expansions to propagate currently assigned labels for nearby
regions. With this localization and spatial propagation, our method can
efficiently infer MRF models with a continuous label space using randomized
search. Our method has several advantages over previous approaches that are
based on fusion moves or belief propagation; it produces submodular moves
deriving a subproblem optimality; it helps find good, smooth, piecewise linear
disparity maps; it is suitable for parallelization; it can use cost-volume
filtering techniques for accelerating the matching cost computations. Even
using a simple pairwise MRF, our method is shown to have best performance in
the Middlebury stereo benchmark V2 and V3.

一篇关于使用基于图割的局部扩展方法的立体匹配技术研究，通过利用局部扩展移动进行实验，该算法提高了 3D 平面标签的准确性，并在 Middlebury 立体基准的 V2 和 V3 版本中表现出最佳性能。