改进的跨尺度引导滤波立体匹配算法
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摘要
作为双目三维重建中的关键步骤,双目立体匹配算法完成了从平面视觉到立体视觉的转化.但如何平衡双目立体匹配算法的运行速度和精度仍然是一个棘手的问题.本文针对现有的局部立体匹配算法在弱纹理、深度不连续等特定区域匹配精度低的问题,并同时考虑到算法实时性,提出了一种改进的跨多尺度引导滤波的立体匹配算法.首先融合AD和Census变换两种代价计算方法,然后采用基于跨尺度的引导滤波进行代价聚合,在进行视差计算时通过制定一个判断准则判断图像中每一个像素点的最小聚合代价对应的视差值是否可靠,当判断对应的视差值不可靠时,对像素点构建基于梯度相似性的自适应窗口,并基于自适应窗口修正该像素点对应的视差值.最后通过视差精化得到最终的视差图.在Middlebury测试平台上对标准立体图像对的实验结果表明,与传统基于引导滤波器的立体匹配算法相比具有更高的精度.
As a key step in binocular 3D reconstruction, the binocular stereo matching algorithm completes the transformation from planar vision to stereo vision. But how to balance the running speed and accuracy of the binocular stereo matching algorithm is still a difficult problem. In this study, focused on that existing local stereo matching algorithm has low matching accuracy in specific regions such as weak texture and depth discontinuity, while considered the real-time performance of the algorithm at the same time, an improved stereo matching algorithm based on cross-scale guided filtering is proposed. Firstly, the two cost calculation methods of SAD and Census transform are combined, and then the cost aggregation is performed by using cross-scale guided filtering. When calculating the disparity calculation, a judgment criterion is used to judge the reliance of the disparity value corresponding to the minimum aggregation cost of each pixel in the image. When it is judged that the corresponding disparity value is unreliable, an adaptive window based on gradient similarity is constructed for the pixel, and the disparity value corresponding to the pixel is corrected based on the adaptive window. Finally, the final disparity map is obtained by parallax refinement. Experimental results on standard stereo image pairs on the Middlebury test platform show higher accuracy than traditional guided filter based stereo matching algorithms.
As a key step in binocular 3D reconstruction, the binocular stereo matching algorithm completes the transformation from planar vision to stereo vision. But how to balance the running speed and accuracy of the binocular stereo matching algorithm is still a difficult problem. In this study, focused on that existing local stereo matching algorithm has low matching accuracy in specific regions such as weak texture and depth discontinuity, while considered the real-time performance of the algorithm at the same time, an improved stereo matching algorithm based on cross-scale guided filtering is proposed. Firstly, the two cost calculation methods of SAD and Census transform are combined, and then the cost aggregation is performed by using cross-scale guided filtering. When calculating the disparity calculation, a judgment criterion is used to judge the reliance of the disparity value corresponding to the minimum aggregation cost of each pixel in the image. When it is judged that the corresponding disparity value is unreliable, an adaptive window based on gradient similarity is constructed for the pixel, and the disparity value corresponding to the pixel is corrected based on the adaptive window. Finally, the final disparity map is obtained by parallax refinement. Experimental results on standard stereo image pairs on the Middlebury test platform show higher accuracy than traditional guided filter based stereo matching algorithms.
引文
1 Scharstein D,Szeliski R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.International Journal of Computer Vision,2002,47(1):7-42.
2 Zabih R,Woodfill J.Non-parametric local transforms for computing visual correspondence.Proceedings of the third European Conference on Computer Vision(Vol.II).Stockholm,Sweden.1994.151-158.
3 Mei X,Sun X,Zhou MC,et al.On building an accurate stereo matching system on graphics hardware.Proceedings of 2011 IEEE International Conference on Computer Vision Workshops.Barcelona,Spain.2011.467-474.
4 Zhu SP,Yan L.Local stereo matching algorithm with efficient matching cost and adaptive guided image filter.The Visual Computer,2017,33(9):1087-1102.[doi:10.1007/s00371-016-1264-6]
5 Yoon KJ,Kweon IS.Adaptive support-weight approach for correspondence search.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656.[doi:10.1109/TPAMI.2006.70]
6 Hosni A,Rhemann C,Bleyer M,et al.Fast cost-volume filtering for visual correspondence and beyond.IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(2):504-511.[doi:10.1109/TPAMI.2012.156]
7 Zhu SP,Yan LN,Li Z.Stereo matching algorithm based on improved Census transform and dynamic programming.Acta Optica Sinica,2016,36(4):0415001.
8 He KM,Sun J,Tang XO.Guided image filtering.IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409.[doi:10.1109/TPAMI.2012.213]
9 Milanfar P.A tour of modern image filtering:New insights and methods,both practical and theoretical.IEEE Signal Processing Magazine,2013,30(1):106-128.[doi:10.1109/MSP.2011.2179329]
10 Zhang K,Fang YQ,Min DB,et al.Cross-scale cost aggregation for stereo matching.IEEE Transactions on Circuits and Systems for Video Technology,2017,27(5):965-976.[doi:10.1109/TCSVT.2015.2513663]
11 Hirschmüller H,Innocent PR,Garibaldi J.Real-time correlation-based stereo vision with reduced border errors.International Journal of Computer Vision,2002,47(1-3):229-246.
12 Scharstein D,Szeliski R,Hirschmüller H.The middlebury stereo vision page.http://vision.middlebury.edu/stereo/.
2 Zabih R,Woodfill J.Non-parametric local transforms for computing visual correspondence.Proceedings of the third European Conference on Computer Vision(Vol.II).Stockholm,Sweden.1994.151-158.
3 Mei X,Sun X,Zhou MC,et al.On building an accurate stereo matching system on graphics hardware.Proceedings of 2011 IEEE International Conference on Computer Vision Workshops.Barcelona,Spain.2011.467-474.
4 Zhu SP,Yan L.Local stereo matching algorithm with efficient matching cost and adaptive guided image filter.The Visual Computer,2017,33(9):1087-1102.[doi:10.1007/s00371-016-1264-6]
5 Yoon KJ,Kweon IS.Adaptive support-weight approach for correspondence search.IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656.[doi:10.1109/TPAMI.2006.70]
6 Hosni A,Rhemann C,Bleyer M,et al.Fast cost-volume filtering for visual correspondence and beyond.IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(2):504-511.[doi:10.1109/TPAMI.2012.156]
7 Zhu SP,Yan LN,Li Z.Stereo matching algorithm based on improved Census transform and dynamic programming.Acta Optica Sinica,2016,36(4):0415001.
8 He KM,Sun J,Tang XO.Guided image filtering.IEEETransactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409.[doi:10.1109/TPAMI.2012.213]
9 Milanfar P.A tour of modern image filtering:New insights and methods,both practical and theoretical.IEEE Signal Processing Magazine,2013,30(1):106-128.[doi:10.1109/MSP.2011.2179329]
10 Zhang K,Fang YQ,Min DB,et al.Cross-scale cost aggregation for stereo matching.IEEE Transactions on Circuits and Systems for Video Technology,2017,27(5):965-976.[doi:10.1109/TCSVT.2015.2513663]
11 Hirschmüller H,Innocent PR,Garibaldi J.Real-time correlation-based stereo vision with reduced border errors.International Journal of Computer Vision,2002,47(1-3):229-246.
12 Scharstein D,Szeliski R,Hirschmüller H.The middlebury stereo vision page.http://vision.middlebury.edu/stereo/.