Learning to Predict Repeatability of Interest Points

Dzung Anh Doan, Daniyar Turmukhambetov, Yasir Latif, Tat-Jun Chin, Soohyun Bae

Abstract:

Many robotics applications require interest points
that are highly repeatable under varying viewpoints and lighting conditions. However, this requirement is very challenging as
the environment changes continuously and indefinitely, leading
to appearance changes of interest points with respect to time.
This paper proposes to predict the repeatability of an interest
point as a function of time, which can tell us the lifespan of
the interest point considering daily or seasonal variation. The
repeatability predictor (RP) is formulated as a regressor trained
on repeated interest points from multiple viewpoints over a
long period of time. Through comprehensive experiments, we
demonstrate that our RP can estimate when a new interest
point is repeated, and also highlight an insightful analysis about
this problem. For further comparison, we apply our RP to
the map summarization under visual localization framework,
which builds a compact representation of the full context
map given the query time. The experimental result shows
a careful selection of potentially repeatable interest points
predicted by our RP can significantly mitigate the degeneration
of localization accuracy from map summarization