“Evaluating And Improving Disparity Maps Without Ground Truth” by Pocol, Istead and Kaplan
Conference:
Type(s):
Title:
- Evaluating And Improving Disparity Maps Without Ground Truth
Session/Category Title: Images, Video & Computer Vision
Presenter(s)/Author(s):
Abstract:
Our method leverages several heuristics to assess the quality of a disparity map for a stereoscopic 3D image pair, without needing to compare against ground truth, and can also infer disparity at pixels with unknown disparity values.
References:
[1]
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[2]
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[3]
Ivan Cabezas, Victor Padilla, and Maria Trujillo. 2012. BMPRE: An Error measure for evaluating disparity maps. In 2012 IEEE 11th International Conference on Signal Processing, Vol. 2. 1051?1055. https://doi.org/10.1109/ICoSP.2012.6491759
[4]
Lesley Istead, Andreea Pocol, Craig S. Kaplan, Isaac Watt, Nick Lemoing, and Alicia Yang. 2021. Generating Rough Stereoscopic 3D Line Drawings from 3D Images. In Proceedings of Graphics Interface 2021 (Virtual Event) (GI 2021). Canadian Information Processing Society, 178 ? 185. https://doi.org/10.20380/GI2021.20
[5]
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[6]
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[7]
Saad Merrouche, Milenko Andria, Boban Bondayulia, and Dimitrije Bujakovia. 2020. Objective Image Quality Measures for Disparity Maps Evaluation. Electronics 9, 10 (2020). https://doi.org/10.3390/electronics9101625
[8]
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[9]
G. Xu, X. Wang, X. Ding, and X. Yang. 2023. Iterative Geometry Encoding Volume for Stereo Matching. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 21919?21928. https://doi.org/10.1109/CVPR52729.2023.02099
