  Numerous applications require robots to operate in environments shared with
other agents, such as humans or other robots. However, such shared scenes are
typically subject to different kinds of long-term semantic scene changes. The
ability to model and predict such changes is thus crucial for robot autonomy.
In this work, we formalize the task of semantic scene variability estimation
and identify three main varieties of semantic scene change: changes in the
position of an object, its semantic state, or the composition of a scene as a
whole. To represent this variability, we propose the Variable Scene Graph
(VSG), which augments existing 3D Scene Graph (SG) representations with the
variability attribute, representing the likelihood of discrete long-term change
events. We present a novel method, DeltaVSG, to estimate the variability of
VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term
dataset, showing notable improvements in this novel task over existing
approaches. Our method DeltaVSG achieves an accuracy of 77.1% and a recall of
72.3%, often mimicking human intuition about how indoor scenes change over
time. We further show the utility of VSG prediction in the task of active
robotic change detection, speeding up task completion by 66.0% compared to a
scene-change-unaware planner. We make our code available as open-source.
