GALA：面向开放词汇高斯泼溅的引导注意力与语言对齐
GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting
AI总结：
GALA提出基于3DGS的开放词汇场景理解框架，通过自监督对比学习和双码本交叉注意力模块实现视角无关语义嵌入，在降低内存的同时支持2D/3D开放词汇查询。
摘要：
3D场景重建与理解日益受到关注，但现有方法仍难以从2D图像中捕获细粒度、语言感知的3D表示。本文提出GALA，一个基于3D高斯泼溅（3DGS）的开放词汇3D场景理解新框架。GALA通过自监督对比学习蒸馏出场景特定的3D实例特征场。为扩展到通用语言特征场，我们引入GALA的核心贡献：一个带有两个可学习码本的交叉注意力模块，用于编码视角无关的语义嵌入。该设计不仅确保实例内特征相似性，还支持无缝的2D和3D开放词汇查询。它通过避免逐高斯高维特征学习来降低内存消耗。在真实世界数据集上的大量实验表明，GALA在2D和3D上均具有出色的开放词汇性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
