Volume 39, Number 4, August 2021
|Page(s)||876 - 882|
|Published online||23 September 2021|
- Zhang Huan, An Li, Zhang Qiang, et al. SGBM algorithm and BM algorithm analysis and research[J]. Geomatics and Spatial Information Technology, 2016, 39(10): 214–216 [Article] [Google Scholar]
- Jac C K M, Byoung T. A dense stereo matching using two-pose dynamic programming with generalized ground control points[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 [Google Scholar]
- Besse F, Roter C, Fitzgibbon A, et al. PMBP: patch match belief propagation for correspondence field estimation[J]. International Journal of Computer Vision, 2014, 110(1): 2–13 [Article] [Google Scholar]
- ŽBONTA J, Lecun Y. Stereo matching by training a convolutional neural network to compare image patches[J]. Journal of Machine Learning Research, 2016, 17(1): 2287–2318 [Article] [Google Scholar]
- Shaked A, Wolf L. Improved stereo matching with constant highway networks and reflective confidence learning[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, 2017 [Google Scholar]
- Xu Xuesong, Wu Junjie. Stereo matching algorithm based on multiscale fusion[J]. Patten Recognition and Artificial Intelligence, 2020, 33(2): 182–187 [Article] [Google Scholar]
- Yan Dengtao. Research on stereo matching algorithm based on convolutional neural network[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2019: 39–50 (in Chinese) [Google Scholar]
- Lin T, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2017 [Google Scholar]
- Scharstein D, Seliski R, Zabih R. A Taxonomy and evaluation of dense two-frame stereo correspondence algorithms[C]//IEEE Stereo and Multi-Baseline Vision, 2002 [Google Scholar]
- Zagoruyko S, Komodakis N. Learning to compare image patches via convolutional neural networks[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015 [Google Scholar]
- Scharstein D, Hirschmvller H, Kitajima Y, et al. High-resolution stereo datasets with subpixel-accurate ground truth[C]//German Conference on Pattern Recognition, Berlin, 2014: 31–42 [Google Scholar]
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