Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps. (arXiv:2206.03799v2 [cs.CV] UPDATED)

Self-supervised monocular depth estimation has been a subject of intense
study in recent years, because of its applications in robotics and autonomous
driving. Much of the recent work focuses on improving depth estimation by
increasing architecture complexity. This paper shows that state-of-the-art
performance can also be achieved by improving the learning process rather than
increasing model complexity. More specifically, we propose (i) only using
invariant pose loss for the first few epochs during training, (ii) disregarding
small potentially dynamic objects …

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