Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting
Recent works on 3D scene understanding leverage 2D masks from visual foundation models (VFMs) to supervise radiance fields, enabling instance-level 3D segmentation. However, the supervision signals from foundation models are not fundamentally object-centric and often require additional mask pre/post-processing or specialized training and loss design to resolve mask identity conflicts across views. The learned identity of the 3D scene is scene-dependent, limiting generalizability across scenes. Therefore, we propose a dataset-level, object-centric supervision scheme to learn object representations in 3D Gaussian Splatting (3DGS). Building on a pre-trained slot attention-based Global Object...
| Authors | Tsuheng Hsu, Guiyu Liu, Juho Kannala et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 6 October 2026 |
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