  We present an end-to-end reconstruction algorithm to build particle
candidates from detector hits in next-generation granular calorimeters similar
to that foreseen for the high-luminosity upgrade of the CMS detector. The
algorithm exploits a distance-weighted graph neural network, trained with
object condensation, a graph segmentation technique. Through a single-shot
approach, the reconstruction task is paired with energy regression. We describe
the reconstruction performance in terms of efficiency as well as in terms of
energy resolution. In addition, we show the jet reconstruction performance of
our method and discuss its inference computational cost. To our knowledge, this
work is the first-ever example of single-shot calorimetric reconstruction of
${\cal O}(1000)$ particles in high-luminosity conditions with 200 pileup.
