ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting

TL;DR - ZeroSplat is a training-free, zero-feature framework for language-guided segmentation of zero, one, or multiple targets in 3D Gaussian Splatting scenes. It improves point-level understanding and efficiency by lifting 2D vision-language priors into 3D through multi-view geometric constraints.

• Introduces the generalized referring 3DGS segmentation task for arbitrary target counts.
• Provides two evaluation benchmarks: GR-LERF and GR-ScanNet.
• Avoids per-scene semantic feature optimization and additional feature storage.
• Reportedly outperforms prior methods in generalized and single-target settings.