  We demonstrate the utility of recurring pattern discovery from a single image
for spatial understanding of a 3D scene in terms of (1) vanishing point
detection, (2) hypothesizing 3D translation symmetry and (3) counting the
number of RP instances in the image.
  Furthermore, we illustrate the feasibility of leveraging RP discovery output
to form a more precise, quantitative text description of the scene. Our
quantitative evaluations on a new 1K+ Recurring Pattern (RP) benchmark with
diverse variations show that visual perception of recurrence from one single
view leads to scene understanding outcomes that are as good as or better than
existing supervised methods and/or unsupervised methods that use millions of
images.
