X-GS：基于3D高斯溅射的感知与思考可扩展框架
X-GS: An Extensible Framework for Perceiving and Thinking with 3D Gaussian Splatting
AI总结：
提出X-GS框架，包含感知器和思考器，统一多种3DGS技术实现实时在线SLAM与语义蒸馏，并支持多模态模型完成下游任务。
摘要：
3D高斯溅射（3DGS）已成为新颖视图合成的强大技术，随后扩展到众多空间AI应用。然而，大多数现有3DGS方法孤立运行，专注于特定领域。本文介绍X-GS，一个包含两个主要组件的可扩展框架。X-GS-感知器统一了广泛的3DGS技术，以实现具有语义蒸馏的实时在线SLAM。X-GS-思考器容纳多模态模型，使其能够与感知器无缝交互以完成下游任务。在我们的X-GS实现中，感知器利用最新的视觉基础模型提高在线SLAM性能，并采用三种关键机制加速语义蒸馏。思考器可以基于对比和生成视觉语言模型构建，并利用感知器的语义高斯溅射解锁3D视觉定位和场景描述等功能。在多个基准上的实验结果表明了X-GS框架的高效性和新解锁的多模态能力。
英文摘要：
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, subsequently extending into numerous spatial AI applications. However, most existing 3DGS methods operate in isolation, focusing on specific domains. In this paper, we introduce X-GS, an extensible framework that integrates previously isolated 3DGS methods into the perception module of a VLM for spatial tasks, with two major components: the $\textit{Perceiver}$ and the $\textit{Thinker}$. The $\textit{Perceiver}$ performs online 3DGS-based SLAM with semantic distillation and outputs semantic Gaussians from unposed video streams. It leverages recent vision foundation models for stronger geometric priors, and we introduce three novel optimizations to improve semantic distillation efficiency. The $\textit{Thinker}$ interfaces diverse VLMs with these semantic Gaussians, unlocking spatial multimodal capabilities such as 3D visual grounding and scene captioning. Experimental results on diverse benchmarks demonstrate the efficiency and newly unlocked multimodal capabilities of the X-GS framework.
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。
