Abstract
We proposed a novel global optimization method based on superpixel for stereo matching in this paper. Comparing with the pixel-based global optimization methods, the matching accuracy of our method is significantly improved. For improving the initial matching cost’s accuracy, we developed an adaptive matching window integrated with shape and size information to build the data term. To ensure the soft constraints of planar disparity distribution, a superpixel-based plane fitting method is introduced to obtain the initial disparity plane. We present a global optimization framework with data term and pixel-based smooth term to refine the disparity results. The experimental results on the Middlebury Stereo Datasets show that our method outperforms some state-of-the-art pixel-based global optimization approaches both quantitatively and qualitatively.
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Jin, H., Liu, S., Zhang, S., Ying, G. (2014). Superpixel-Based Global Optimization Method for Stereo Disparity Estimation. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_46
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DOI: https://doi.org/10.1007/978-3-662-45646-0_46
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