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MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation
To address this issue, we present Motion-Aware Loss, which leverages the temporal relation among consecutive input frames and a novel distillation scheme between the teacher and student networks in the multi-frame self-supervised depth estimation methods.
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