  Object recognition in 3D point clouds is a challenging task, mainly when time
is an important factor to deal with, such as in industrial applications. Local
descriptors are an amenable choice whenever the 6 DoF pose of recognized
objects should also be estimated. However, the pipeline for this kind of
descriptors is highly time-consuming. In this work, we propose an update to the
traditional pipeline, by adding a preliminary filtering stage referred to as
saliency boost. We perform tests on a standard object recognition benchmark by
considering four keypoint detectors and four local descriptors, in order to
compare time and recognition performance between the traditional pipeline and
the boosted one. Results on time show that the boosted pipeline could turn out
up to 5 times faster, with the recognition rate improving in most of the cases
and exhibiting only a slight decrease in the others. These results suggest that
the boosted pipeline can speed-up processing time substantially with limited
impacts or even benefits in recognition accuracy.
