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arXiv · 2508.14278

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

Elena Alegret·Kunyi Li·Sen Wang·Siyun Liang·Michael Niemeyer·Stefano Gasperini·Nassir Navab·Federico Tombari
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Abstract

3D scene reconstruction and understanding have gained increasing popularity, yet existing methods still 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 extend to generalized language feature fields, we introduce the 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.

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Elena Alegret, Kunyi Li, Sen Wang, Siyun Liang, Michael Niemeyer, Stefano Gasperini, Nassir Navab, Federico Tombari. 2025-08-19. GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting. https://arxiv.org/abs/2508.14278

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