Depth Flow Separate learning Joint learning (Ours) Fig. 1 : Joint learning v.s. separate learning. Single-view depth prediction and optical flow estimation are two highly correlated tasks. Existing work, however, often addresses these two tasks in isolation. In this paper, we propose a novel cross-task consistency loss to couple the training of these two problems using unlabeled monocular videos. Through enforcing the underlying geometric constraints, we show substantially improved results for both tasks. Abstract. We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the predicted scene depth and camera motion to synthesize 2D optical flow by backprojecting the induced 3D scene flow. The discrepancy between the rigid flow (from depth prediction and camera motion) and the estimated flow (from optical flow model) allows us to impose a cross-task consistency loss. While all the networks are jointly optimized during training, they can be applied independently at test time. Extensive experiments demonstrate that our depth and flow models compare favorably with state-of-the-art unsupervised methods.
Zou et al. REF exploit consistency between depth and optical flow to improve performance.
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
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Computer Science