VS-Graphs: Environment-Aware 3D Scene Graphs for Visual SLAM

Abstract

We introduce the latest achievements and results of Visual S-Graphs (vS-Graphs), our open-source, real-time VSLAM framework that tightly couples map reconstruction to online 3D scene graph generation. vS-Graphs employs visual and depth cues to detect and localize building components, such as walls and ground surfaces, from which higher-level structural elements, including variant-shaped rooms and floors, are inferred. These entities are incorporated into an optimizable hierarchical 3D scene graph, jointly maintained with the SLAM pipeline, enabling richer map semantics and improved localization. The framework is publicly available at https://github.com/snt-arg/visual_sgraphs. We evaluated vS-Graphs on both public RGB-D benchmarks and our in-house SMapper dataset, which includes diverse multi-room indoor environments with LiDAR-derived ground truth. These evaluations focused on trajectory estimation, map quality, semantic structural detection, and runtime performance. The results highlight the potential of tightly coupling VSLAM with online hierarchical scene graph generation for richer, more structurally meaningful environmental understanding. In particular, the ability of vS-Graphs to infer higher-level layout entities from visually detected building components suggests a promising direction for bridging geometric mapping and semantic scene reasoning within a unified framework. Full evaluation results and figures are available on https://snt-arg.github.io/vsgraphs-results/.