ROOM-3D: Real-Time Unsupervised Online 3D Room Segmentation

Abstract

Room-level understanding is essential for mobile robots operating in unseen indoor environments. Existing room segmentation methods predominantly assume an offline setting, typically requiring a complete scene reconstruction before producing the final result, which limits their applicability to real-time robotic navigation. In this work, we introduce the novel problem of emph{online 3D room segmentation}, where a robot must continuously segment rooms and detect room transitions from streaming sensory observations during exploration. By framing 3D room segmentation in an online setting, we aim to encourage further research in practical, real-time semantic mapping for autonomous agents operating in unknown environments. To properly assess this novel online setting, we also introduce instantaneous evaluation metrics tailored to online room segmentation and transition detection. We also propose textbf{ROOM-3D}: a real-time unsupervised framework, for the problem of online 3D room segmentation. textbf{ROOM-3D} combines Gaussian-based SLAM with open-vocabulary semantic reasoning to incrementally generate a semantically structured 3D room segmentations, as well as transition estimates, without access to future observations or global post-processing. Experiments on HM3D-Semantics dataset demonstrate that ROOM-3D achieves temporally consistent and accurate segmentation under strict online constraints, while offering state-of-the-art results for the offline experimental evaluation.