Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for
  Consistent Self-Supervised Monocular Depth Estimation   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.

33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn