AI 中文总结
OutLangSplat改进特征表示与聚合策略，构建首个无人机室外开放词汇3D场景理解数据集，在开放词汇语义分割与实例定位任务上优于SOTA方法。
AI 中文摘要
三维语言高斯溅射（3D Language Gaussian Splatting）将开放词汇语言特征嵌入三维高斯溅射（3D Gaussian Splatting），为文本驱动的三维场景理解提供高效的显式表示。然而，现有方法仅适用于室内或小规模场景，在无人机（Unmanned Aerial Vehicle, UAV）室外场景中往往失效，该场景下严重的遮挡和远距离视角常导致语义激活错误与目标响应缺失。本文提出OutLangSplat，通过改进特征表示与聚合可靠性，使语言高斯表示适配无人机室外场景。在特征表示方面，设计了基于区域对齐与融合的二维-三维双分支表示，以提升空间一致性，减少不完整目标响应与背景误激活；在特征聚合方面，引入无需训练的贡献与一致性感知高斯特征聚合策略，利用像素贡献可靠性与跨视图语义一致性，抑制噪声视角的不可靠响应。本文通过对四个真实世界公开无人机室外场景数据集上的各类目标进行人工标注，提供了新的数据集，据所知，这是首个可用于无人机室外场景开放词汇三维场景理解的公开数据集。定量评估与消融研究表明，OutLangSplat在开放词汇语义分割与实例定位任务上均优于现有最优（SOTA）方法，数据集与代码将开源。
英文摘要
3D Language Gaussian Splatting embeds open-vocabulary language features into 3D Gaussian Splatting, providing an efficient explicit representation for text-driven 3D scene understanding. However, existing methods are limited to indoor or small-scale scenes, and tend to fail in Unmanned Aerial Vehicle (UAV) outdoor scenes, where severe occlusions and long distance viewpoints often lead to incorrect semantic activations and missing target responses. In this paper, we present OutLangSplat which adapts language Gaussian representations to UAV outdoor scenes by improving feature representation and aggregation reliability. For the feature representation, a 2D-3D dual-branch representation with region-based alignment and fusion is designed to improve spatial consistency, reducing incomplete target responses and background misactivations. For the feature aggregation, we introduce a training-free contribution and consistency-aware Gaussian feature aggregation strategy that leverages pixel contribution reliability and cross-view semantic consistency to suppress unreliable responses from noisy viewpoints. A new dataset is provided by manually annotating various objects on four real-world public UAV outdoor scene datasets. To the best of our knowledge, it is the first accessible dataset of open-vocabulary 3D scene understanding for UAV outdoor scenes. Quantitative evaluations and ablation studies demonstrate that OutLangSplat outperforms SOTA methods on both open-vocabulary semantic segmentation and instance localization tasks. The datasets and codes will be open-sourced.
Comments9 pages, 6 figures, 7 tables
