ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting
Ranking
Overall
61
Content
70
Popularity
40
Observed public metrics from 1 member.
Merged summary
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.
Sources (1)
ZeroSplat: Generalized Referring Segmentation in 3D Gaussian Splatting
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
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.