Abstract: While ground truth depth data remains hard to obtain, self-supervised monocular depth estimation methods enjoy growing attention. Much research in this area aims at improving loss functions or network architectures. Most works, however, do not leverage self-supervision to its full potential. They stick to the standard closed world train-test pipeline, assuming the network parameters to be fixed after the training is finished. Such an assumption does not allow to adapt to new scenes, whereas with self-supervision this becomes possible without ex...
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Topics: 
Artificial intelligence
Machine learning
Computer vision