SwitchSplat: Learning Discrete Object States from Casually Captured Scenes
3D Gaussian Splatting has made high-fidelity 3D reconstruction fast and accessible, yet existing representations assume a static world. This assumption breaks down in casual captures, where a user naturally interacts with the environment -- opening doors, toggling switches, or moving objects -- introducing discrete state changes that no current framework can model. Time-conditioned dynamic methods such as 4DGS conflate when a state was observed with what state the scene is in, limiting them to replaying seen sequences rather than composing arbitrary object configurations. We present SwitchSplat, the first 3DGS representation to address this gap via a base+slot decomposition: a shared stat...
| Authors | Yizheng Xie, Yalda Foroutan, George Shramko et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 20 October 2026 |
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