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• W3160991246 abstract "This paper proposes a novel deep neural network-based method for stereo image rectification. The neural network is mainly based on the theoretical basis of epipolar constraints from multi-view geometry and intensity constraints of images, which separately describes the relationship of the corresponding epipolar lines between a pair of image, including the epipolar-line slope and y-intercept consistency of the epipolar lines and the consistency of the corresponding intensity values between two images. Benefiting from the designed rectification framework together with a feature matching module to extract accurate corresponding key-points between views, our method is able to realize a stable and accurate stereo rectification process. Compared with classic feature-based rectification methods, our proposed method can rectify small errors, and achieve a much more accurate rectification performance. Experiments conducted on synthetic face dataset and real-world KITTI dataset demonstrate the effectiveness and robustness of the proposed method." @default.
• W3160991246 created "2021-05-24" @default.
• W3160991246 creator A5008033862 @default.
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• W3160991246 date "2021-06-06" @default.
• W3160991246 modified "2023-10-01" @default.
• W3160991246 title "Stereo Rectification Based on Epipolar Constrained Neural Network" @default.
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• W3160991246 doi "https://doi.org/10.1109/icassp39728.2021.9413735" @default.
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