CoopNet improves co-training of depth, odometry, and optical flow networks via a hybrid loss that rebalances gradients by modeling photometric error disagreements to identify and ignore moving objects.
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Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions
CoopNet improves co-training of depth, odometry, and optical flow networks via a hybrid loss that rebalances gradients by modeling photometric error disagreements to identify and ignore moving objects.