Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter. (arXiv:2208.01489v1 [cs.CV])

This paper presents an open and comprehensive framework to systematically
evaluate state-of-the-art contributions to self-supervised monocular depth
estimation. This includes pretraining, backbone, architectural design choices
and loss functions. Many papers in this field claim novelty in either
architecture design or loss formulation. However, simply updating the backbone
of historical systems results in relative improvements of 25%, allowing them to
outperform the majority of existing systems. A systematic evaluation of papers
in this field was not straightforward. The need to compare …

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