MSCS: MeshStereo with Cross-Scale Cost Filtering for fast stereo matching
MeshStereo (MS) and cross-scale cost filtering (CSCF) are two most recently celebrated models for stereo matching. On one hand, MS model enlightens for fast solving the dense stereo correspondence problem according to a region-based opinion. On the other hand, CSCF model could generate more robust matching cost volumes than single scale. In this study, the authors weave these two models together for attaining greater and faster disparity estimation. With CSCF, more powerful initial volumes of matching cost are computed and they are conducted as the data term of MS energy function model. More importantly, the novel-fused stereo model also draws a closer connection between multi-scale aggregated and global algorithms. Integrating the advantages of both stereo models, they name the presented one as MS with cross-scale (MSCS). Performance evaluations on Middlebury v.2 and v.3 stereo data sets demonstrate that the proposed MSCS outperforms other four most challenging stereo matching algorithms; and also performs better on Microsoft i2i stereo videos. In addition, thanks to this novel-fused model, MSCS requires fewer iteration times for optimising and makes it surprisingly possesses a much faster execution time.