AnythingReality: Robust Online Gaussian Splatting SLAM for Open-Vocabulary VR Scene Exploration
Timofei Kozlov, Dmitrii Maliukov, Andrey Marchenko, Miguel Altamirano Cabrera, Dzmitry Tsetserukou
Abstract
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.