  3D instance segmentation remains a challenging problem in computer vision.
Particle tracking at colliders like the LHC can be conceptualized as an
instance segmentation task: beginning from a point cloud of hits in a particle
detector, an algorithm must identify which hits belong to individual particle
trajectories and extract track properties. Graph Neural Networks (GNNs) have
shown promising performance on standard instance segmentation tasks. In this
work we demonstrate the applicability of instance segmentation GNN
architectures to particle tracking; moreover, we re-imagine the traditional
Cartesian space approach to track-finding and instead work in a conformal
geometry that allows the GNN to identify tracks and extract parameters in a
single shot.
