arXiv:2409. 11972v4 Announce Type: replace-cross Abstract: Enabling robots to autonomously discover high-level spatial concepts (e.
arXiv:2608.28733v1 Announce Type: cross Abstract: Indoor 3D Scene Graphs (3DSGs) represent environments as multi-layer hierarchies that connect observed geometric primitives (e.g., planes) to higher-...
arXiv:2606. 06721v1 Announce Type: cross Abstract: Robots that operate over extended periods should not merely visit space; they should progressively understand it.
Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extraction frameworks typically rely on purely local visual clustering or strict geometric heuristics, such as wall-separated rooms, which fail in open-plan or arbitrarily-structured environments.
VoxelFix is a graph‑based post‑hoc semantic correction method that refines voxel labels in completed 3D voxel maps while preserving their geometry and occupancy. It learns to correct errors by exploiting local geometry and neighboring semantic information, using training pairs generated by corrupting annotated maps with class confusions from upstream perception pipelines. Experiments on OccuFly maps show consistent improvements of 4.23–5.00 percentage points in mIoU, especially for tree, roof, and wall classes, and the method generalizes to out‑of‑distribution aerial scenes.
arXiv:2609.38620v1 Announce Type: new Abstract: Neural implicit representations have had a significant impact on scene reconstruction by enabling robots to build continuous, differentiable, and high-...
arXiv:2609.22351v1 Announce Type: new Abstract: Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a ro...
arXiv:2601. 10168v3 Announce Type: replace-cross Abstract: Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-image aggregation under occlusions and constrained viewpoints.
arXiv:2606. 29786v2 Announce Type: replace Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments.
DGSG-Mind introduces a hybrid instance-aware 3D Gaussian dynamic scene graph system that integrates open‑vocabulary semantic information into dynamic 3D scene representations. By coupling a probabilistic voxel grid with explicit 3D Gaussians, it achieves robust cross‑modal instance fusion, incremental semantic mapping, and dynamic change handling through Gaussian‑based relocalization and masked refinement. The system builds a hierarchical scene graph and a 3D Gaussian Mind for multimodal reasoning, achieving state‑of‑the‑art zero‑shot 3D visual grounding and strong performance in open‑vocabulary semantic segmentation and scene reconstruction, and is demonstrated on real‑world robots.
arXiv:2512.03284v2 Announce Type: replace Abstract: Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-s...
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.
Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D graph. Existing online methods treat this fusion as a fully deterministic pipeline, where we identify three sources of uncertainty that are overlooked: observation, 2D model, and 3D representation.