  Understanding the 3D world from 2D images involves more than detection and
segmentation of the objects within the scene. It also includes the
interpretation of the structure and arrangement of the scene elements. Such
understanding is often rooted in recognizing the physical world and its
limitations, and in prior knowledge as to how similar typical scenes are
arranged. In this research we pose a new challenge for neural network (or
other) scene understanding algorithms - can they distinguish between plausible
and implausible scenes? Plausibility can be defined both in terms of physical
properties and in terms of functional and typical arrangements. Hence, we
define plausibility as the probability of encountering a given scene in the
real physical world. We build a dataset of synthetic images containing both
plausible and implausible scenes, and test the success of various vision models
in the task of recognizing and understanding plausibility.
