Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2409. 11972v4 Announce Type: replace-cross Abstract: Enabling robots to autonomously discover high-level spatial concepts (e.
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.
arXiv:2606. 29786v2 Announce Type: replace Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments.
arXiv:2607. 00889v1 Announce Type: cross Abstract: We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences.
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.
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.