Abstract
Stereo matching is a challenging problem due to the mismatches caused by difficult environment conditions. In this paper, we propose an enhanced version of our previous work, denoted as 3DMST-CM, to handle challenging cases and obtain a high-accuracy disparity map based on the ambiguity of image pixels. We develop a module of distinctiveness analysis to classify pixels into distinctive and ambiguous pixels. Then distinctive pixels are utilized as anchor pixels to help match ambiguous pixels accurately. The experimental results demonstrate the effectiveness of our method and reach state-of-the-art on the Middlebury 3.0 benchmark.
D. Xu—Equally contributed to this work.
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Xiao, Y. et al. (2020). Confidence Map Based 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_25
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