We present a novel integrated architecture for robust online 3D Gaussian splatting, real-time VR exploration, and speech-driven Vision-Language-Model interaction. Unlike methods assuming clean depth or external poses, our system combines ORB-SLAM3-based pose estimation with online Gaussian reconstruction for noisy real-world data. A VR pipeline enables immersive exploration of incremental reconstructions; a semantic module transcribes voice commands, generates scene descriptions, and records points of interest. Against state-of-the-art online Gaussian splatting methods, we improve image quality on our dataset (+14.5% PSNR, +8.6% SSIM, -14.3% LPIPS) and TUM-RGBD (+11.7% PSNR, +7.8% SSIM, -21.6% LPIPS), with comparable or superior frame rates via quality-speed configurations. We achieve an 88% VLM object-recognition rate.
万物现实：面向开放词汇虚拟现实场景探索的鲁棒在线高斯泼溅SLAM系统 / AnythingReality: Robust Online Gaussian Splatting SLAM for Open-Vocabulary VR Scene Exploration
本文提出了一种集成了实时3D重建、虚拟现实（VR）导航和语音驱动视觉语言模型（VLM）交互的稳健系统，能够处理真实世界中的噪声数据，比现有在线高斯泼溅方法在图像质量上提升显著（最高提升14.5%），并达到88%的物体识别准确率。
源自 arXiv: 2607.09260
