Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
This addresses the challenge of object detection in sparse or irregular data structures for fields like high-energy physics and computer vision, offering a more generalizable approach beyond traditional image-based methods.
The paper tackles the problem of detecting an unknown number of objects in sparse or non-image data like physics detectors, graphs, and point clouds, proposing the object condensation method which achieves multi-object reconstruction without grid-based or density assumptions, as demonstrated in particle reconstruction compared to classic approaches.
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.