标题： 高斯VLM：使用语言对齐的高斯点云进行场景中心的3D视觉语言模型用于具身推理及其他应用 显示英文标题

标题： GaussianVLM: Scene-centric 3D Vision-Language Models using Language-aligned Gaussian Splats for Embodied Reasoning and Beyond

摘要： 随着多模态语言模型的进步，其在3D场景理解中的应用是一个快速发展的前沿领域，推动了3D视觉-语言模型（VLMs）的发展。 当前的方法表现出对物体检测器的强烈依赖，引入了处理瓶颈和分类灵活性的限制。 为了解决这些限制，我们提出了一种以场景为中心的3D VLM，用于3D高斯点云场景，该模型采用语言和任务感知的场景表示。 我们的方法通过将语言与每个高斯基元相关联，直接将丰富的语言特征嵌入到3D场景表示中，实现了早期模态对齐。 为了处理生成的密集表示，我们引入了一个双稀疏化器，通过任务引导和位置引导的路径将它们提炼为紧凑的任务相关标记，生成稀疏的任务感知全局和局部场景标记。 值得注意的是，我们提出了第一个基于高斯点云的VLM，利用从标准RGB图像中得出的逼真3D表示，在域外设置中展示了强大的泛化能力：它将先前3D VLM的性能提高了五倍。 显示英文摘要

摘要： As multimodal language models advance, their application to 3D scene understanding is a fast-growing frontier, driving the development of 3D Vision-Language Models (VLMs). Current methods show strong dependence on object detectors, introducing processing bottlenecks and limitations in taxonomic flexibility. To address these limitations, we propose a scene-centric 3D VLM for 3D Gaussian splat scenes that employs language- and task-aware scene representations. Our approach directly embeds rich linguistic features into the 3D scene representation by associating language with each Gaussian primitive, achieving early modality alignment. To process the resulting dense representations, we introduce a dual sparsifier that distills them into compact, task-relevant tokens via task-guided and location-guided pathways, producing sparse, task-aware global and local scene tokens. Notably, we present the first Gaussian splatting-based VLM, leveraging photorealistic 3D representations derived from standard RGB images, demonstrating strong generalization: it improves performance of prior 3D VLM five folds, in out-of-the-domain settings.