  High-energy physics detectors, images, and point clouds share many
similarities in terms of object detection. However, while detecting an unknown
number of objects in an image is well established in computer vision, even
machine learning assisted object reconstruction algorithms in particle physics
almost exclusively predict properties on an object-by-object basis. Traditional
approaches from computer vision either impose implicit constraints on the
object size or density and are not well suited for sparse detector data or rely
on objects being dense and solid. The object condensation method proposed here
is independent of assumptions on object size, sorting or object density, and
further generalises to non-image-like data structures, such as graphs and point
clouds, which are more suitable to represent detector signals. The pixels or
vertices themselves serve as representations of the entire object, and a
combination of learnable local clustering in a latent space and confidence
assignment allows one to collect condensates of the predicted object properties
with a simple algorithm. As proof of concept, the object condensation method is
applied to a simple object classification problem in images and used to
reconstruct multiple particles from detector signals. The latter results are
also compared to a classic particle flow approach.
