The existing stereo refinement methods optimize a surface representation using a multi-view photo-consistency functional. Such optimization is iterative and requires repeated computation of gradients over all surface regions, which is the bottleneck affecting adversely the computational efficiency of the refinement. In this paper, we present a flexible and efficient framework for mesh surface refinement in multi-view stereo. The newly proposed Adaptive Resolution Control (ARC) evaluates an optimal trade-off between the geometry accuracy and the performance via curve analysis. Then, it classifies the regions into the significant and insignificant ones using a graph-cut optimization. After that, each region is subdivided and simplified accordingly in the remaining refinement process, producing a triangular mesh in adaptive resolutions. Consequently, the ARC accelerates the stereo refinement by severalfold by culling out most insignificant regions, while still maintaining a similar level of geometry details that the state-of-the-art methods could achieve. We have implemented the ARC and demonstrated intensively on both public benchmarks and private datasets, which all confirm the effectiveness and the robustness of the ARC.
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