  Inferring geometrically consistent dense 3D scenes across a tuple of
temporally consecutive images remains challenging for self-supervised monocular
depth prediction pipelines. This paper explores how the increasingly popular
transformer architecture, together with novel regularized loss formulations,
can improve depth consistency while preserving accuracy. We propose a spatial
attention module that correlates coarse depth predictions to aggregate local
geometric information. A novel temporal attention mechanism further processes
the local geometric information in a global context across consecutive images.
Additionally, we introduce geometric constraints between frames regularized by
photometric cycle consistency. By combining our proposed regularization and the
novel spatial-temporal-attention module we fully leverage both the geometric
and appearance-based consistency across monocular frames. This yields
geometrically meaningful attention and improves temporal depth stability and
accuracy compared to previous methods.
