  The extraction and matching of interest points is a prerequisite for many
geometric computer vision problems. Traditionally, matching has been achieved
by assigning descriptors to interest points and matching points that have
similar descriptors. In this paper, we propose a method by which interest
points are instead already implicitly matched at detection time. With this,
descriptors do not need to be calculated, stored, communicated, or matched any
more. This is achieved by a convolutional neural network with multiple output
channels and can be thought of as a collection of a variety of detectors, each
specialized to specific visual features. This paper describes how to design and
train such a network in a way that results in successful relative pose
estimation performance despite the limitation on interest point count. While
the overall matching score is slightly lower than with traditional methods, the
approach is descriptor free and thus enables localization systems with a
significantly smaller memory footprint and multi-agent localization systems
with lower bandwidth requirements. The network also outputs the confidence for
a specific interest point resulting in a valid match. We evaluate performance
relative to state-of-the-art alternatives.
