# 3-D Scene Graph: A Sparse and Semantic Representation of Physical Environments for Intelligent Agents

arXiv: https://arxiv.org/abs/1908.04929 · HTML: https://mlsanity.com/paper/1908.04929/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 30 of 74)

- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning](https://mlsanity.com/paper/2411.17735/) (2024)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [GRUtopia: Dream General Robots in a City at Scale](https://mlsanity.com/paper/2407.10943/) (2024)
- [UniGoal: Towards Universal Zero-shot Goal-oriented Navigation](https://mlsanity.com/paper/2503.10630/) (2025)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion](https://mlsanity.com/paper/2305.16283/) (2023)
- [SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments](https://mlsanity.com/paper/2309.04077/) (2023)
- [Incremental 3D Semantic Scene Graph Prediction from RGB Sequences](https://mlsanity.com/paper/2305.02743/) (2023)
- [GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering](https://mlsanity.com/paper/2412.14480/) (2024)
- [Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs](https://mlsanity.com/paper/2309.15940/) (2023)
- [Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph](https://mlsanity.com/paper/2406.07113/) (2024)
- [SGAligner : 3D Scene Alignment with Scene Graphs](https://mlsanity.com/paper/2304.14880/) (2023)
- [SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs](https://mlsanity.com/paper/2404.00469/) (2024)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation](https://mlsanity.com/paper/2405.05792/) (2024)
- [FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction](https://mlsanity.com/paper/2503.07909/) (2025)
- [4D Panoptic Scene Graph Generation](https://mlsanity.com/paper/2405.10305/) (2024)
- [Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI](https://mlsanity.com/paper/2311.03783/) (2023)
- [DANLI: Deliberative Agent for Following Natural Language Instructions](https://mlsanity.com/paper/2210.12485/) (2022)
- [Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding](https://mlsanity.com/paper/2309.15065/) (2023)
- [Neural Trees for Learning on Graphs](https://mlsanity.com/paper/2105.07264/) (2021)
- [MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2511.10376/) (2025)
- [CrossOver: 3D Scene Cross-Modal Alignment](https://mlsanity.com/paper/2502.15011/) (2025)
- [Towards Scene Graph Anticipation](https://mlsanity.com/paper/2403.04899/) (2024)
- [3D Question Answering for City Scene Understanding](https://mlsanity.com/paper/2407.17398/) (2024)

## Abstract (arXiv)

Intelligent agents gather information and perceive semantics within the environments before taking on given tasks. The agents store the collected information in the form of environment models that compactly represent the surrounding environments. The agents, however, can only conduct limited tasks without an efficient and effective environment model. Thus, such an environment model takes a crucial role for the autonomy systems of intelligent agents. We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability. Although a number of researchers have attempted to develop such models that represent environments precisely to a certain degree, they lack broad applicability, intuitive usability, and satisfactory scalability. To tackle these limitations, we propose 3-D scene graph as an environment model and the 3-D scene graph construction framework. The concise and widely used graph structure readily guarantees usability as well as scalability for 3-D scene graph. We demonstrate the accuracy and applicability of the 3-D scene graph by exhibiting the deployment of the 3-D scene graph in practical applications. Moreover, we verify the performance of the proposed 3-D scene graph and the framework by conducting a series of comprehensive experiments under various conditions.