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• W2966927056 abstract "In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel Feedback Loop'' to connect the network input with output, LidarStereoNet could tackle both noisy Lidar points and misalignment between sensors that have been ignored in existing Lidar-stereo fusion studies. Besides, we propose to incorporate a piecewise planar model into network learning to further constrain depths to conform to the underlying 3D geometry. Extensive quantitative and qualitative evaluations on both real and synthetic datasets demonstrate the superiority of our method, which outperforms state-of-the-art stereo matching, depth completion and Lidar-Stereo fusion approaches significantly." @default.
• W2966927056 created "2019-08-22" @default.
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• W2966927056 date "2019-06-01" @default.
• W2966927056 modified "2023-10-01" @default.
• W2966927056 title "Noise-Aware Unsupervised Deep Lidar-Stereo Fusion" @default.
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• W2966927056 doi "https://doi.org/10.1109/cvpr.2019.00650" @default.
• W2966927056 hasPublicationYear "2019" @default.
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