Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data

High-energy physics detectors, images, and point clouds share many similarities as far as object detection is concerned. 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. One of the reasons is that traditional approaches to deep-neural network based multi-object detection usually employ anchor boxes, imposing implicit constraints on object sizes and density, which are not well suited for highly sparse detector data with differences in densities spanning multiple orders of magnitude. Other approaches rely heavily on objects being dense and solid, with well defined edges and a central point that is used as a keypoint to attach properties. This approach is also not directly applicable to generic detector signals. 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.