On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion. (arXiv:2106.04007v2 [cs.CV] UPDATED)

Structure from motion (SfM) has recently been formulated as a self-supervised
learning problem, where neural network models of depth and egomotion are
learned jointly through view synthesis. Herein, we address the open problem of
how to best couple, or link, the depth and egomotion network components, so
that information such as a common scale factor can be shared between the
networks. Towards this end, we introduce several notions of coupling,
categorize existing approaches, and present a novel tightly-coupled approach
that …

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