Publikation
Multi-Object 2DGS: Occlusion-Robust Background Removal for Multiple Object Models
Marcel Rogge; Didier Stricker
In: International Conference on Pattern Recognition Applications and Methods 2025. International Conference on Pattern Recognition Applications and Methods (ICPRAM-2025), February 23-25, Porto, Portugal, Pages 151-178, LNCS, Vol. 16555, ISBN 978-3-032-28661-1, Springer, 2026.
Zusammenfassung
Most Gaussian Splatting methods focus on the reconstruction of entire scenes and lack the ability to focus the reconstruction on specific objects. This makes them computationally expensive and requires labor-intensive post-processing for object-centric applications. While a recent method makes object-centric reconstruction possible, it does not perform well for datasets with strong inter-object occlusions. Additionally, it does not support the reconstruction of multiple objects within a single scene without cumbersome sequential processing. We propose a substantial extension that enables object-centric reconstruction of multiple objects in parallel by leveraging instance segmentation. We also propose an improved loss term that shows resilience against inter-object occlusions. Our proposed method reconstructs multiple object models as object-centric Gaussian representations, which can be exported as meshes. Our improvements increase quality by up to 88% while reducing training time by up to 29% on scenes with a large number of objects and many inter-object occlusions.