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Loop-Net: Joint Unsupervised Disparity and Optical Flow Estimation of Stereo Videos With Spatiotemporal Loop Consistency

Most of existing deep learning-based depth and optical flow estimation methods require the supervision of a lot of ground truth data, and hardly generalize to video frames, resulting in temporal inconsistency. In this letter, we propose a joint framework that estimates disparity and optical flow of stereo videos and generalizes across various video frames by considering the spatiotemporal relation between the disparity and flow without supervision. To improve both accuracy and consistency, we propose a loop consistency loss which enforces the spatiotemporal consistency of the estimated disparity and optical flow. Furthermore, we introduce a video-based training scheme using the c-LSTM to reinforce the temporal consistency. Extensive experiments show our proposed methods not only estimate disparity and optical flow accurately but also further improve spatiotemporal consistency. Our framework outperforms the state-of-the-art unsupervised depth and optical flow estimation models on the KITTI benchmark dataset.