End-to-End Deep Learning Method with Disparity Correction for Stereo Matching
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-023-07985-5.pdf
Reference36 articles.
1. Ma, N.; Men, Y.; Men, C., et al.: Segmentation-based stereo matching using combinatorial similarity measurement and adaptive support region. Optik 137, 124–134 (2017)
2. Shahbazi, M.; Sohn, G.; Théau, J.: High-density stereo image matching using intrinsic curves. ISPRS J. Photogramm. Remote. Sens. 146, 373–388 (2018)
3. Tan, X.; Sun, C.; Pham, T.D.: Stereo matching based on multi-direction polynomial model. Signal Process. Image Commun. 44, 44–56 (2016)
4. Hong, G.-S.; Kim, B.-G.: A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range images. Displays 49, 80–87 (2017)
5. Zeglazi, O.; Rziza, M.; Amine, A., et al.: A hierarchical stereo matching algorithm based on adaptive support region aggregation method. Pattern Recogn. Lett. 112, 205–211 (2018)
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