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GALA: Guided Attention With Language Alignment for Open Vocabulary Gaussian Splatting

MCML Authors

Abstract

3D scene reconstruction and understanding have gained increasing popularity, yet existing methods struggle to capture fine-grained, language-aware 3D representations from 2D images. In this paper, we present GALA, a novel framework for open-vocabulary 3D scene understanding with 3D Gaussian Splatting (3DGS). GALA distills a scene-specific 3D instance feature field via self-supervised contrastive learning. To further extend this to generalized language feature fields, we introduce a core contribution of GALA, a cross-attention module with two learnable codebooks that encode view-independent semantic embeddings. This design not only ensures intra-instance feature similarity but also supports seamless 2D and 3D open-vocabulary queries. It reduces memory consumption by avoiding per-Gaussian high-dimensional feature learning. Extensive experiments on real-world datasets demonstrate GALA's remarkable open-vocabulary performance on both 2D and 3D.

inproceedings ALW+26

3DV 2026

13th International Conference on 3D Vision. Vancouver, Canada, Mar 20-23, 2026. To be published. Preprint available.

Authors

E. Alegret • K. Li • S. Wang • S. Liang • M. Niemeyer • S. Gasperini • N. Navab • F. Tombari

Links

URL

In Collaboration

 Google
 VisualAIs Labs

Research Area

 C1 | Medicine

BibTeXKey: ALW+26

#p-navab #p-tombari
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