  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 leverages the interdependence of depth and egomotion at training time and
at test time. Our approach uses iterative view synthesis to recursively update
the egomotion network input, permitting contextual information to be passed
between the components. We demonstrate through substantial experiments that our
approach promotes consistency between the depth and egomotion predictions at
test time, improves generalization, and leads to state-of-the-art accuracy on
indoor and outdoor depth and egomotion evaluation benchmarks.
