ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 227-234, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-227-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
GLOBAL PATCH MATCHING
X. Huang1,2, K. Hu3, X. Ling4, Y. Zhang4, Z. Lu1, and G. Zhou1 1Wuhan Engineering Science & Technology Institute, Jiangda Road 30, Wuhan, China
2Wuhan Sense Tour Tecnhonogy Company, Luoshi South Road 320, Wuhan, China
3Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
4School of Remote Sensing and Information Engineering, Wuhan University, China
Keywords: Patch Matching, Matrix Energy Function, Global Optimization, Fronto-parallel Bias, 3D Label Algorithm Abstract. This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces.
Conference paper (PDF, 1026 KB)


Citation: Huang, X., Hu, K., Ling, X., Zhang, Y., Lu, Z., and Zhou, G.: GLOBAL PATCH MATCHING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 227-234, https://doi.org/10.5194/isprs-annals-IV-2-W4-227-2017, 2017.

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