Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
- URL: http://arxiv.org/abs/2605.15753v1
- Date: Fri, 15 May 2026 09:14:50 GMT
- Title: Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
- Abstract summary: We extend the benchmark coverage by introducing dense tabletop objects and explicit multi-level functional relationships.<n>This work introduces critical challenges involving small-scale, dense, and similar instances.<n>We propose an open-vocabulary pipeline based on 2D visual grounding and 3D graph optimization.
- Score: 116.57196064763924
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Functional 3D scene graphs offer a versatile and flexible representation for 3D scene understanding and robotic manipulation, defined by object nodes, interactive elements, and functional relationship edges. However, their potential remains underexplored due to the limited coverage of existing benchmarks and the overly straightforward design of previous pipelines, which primarily focus on large-scale furniture but lack of hierarchical structures. Therefore, in this work, we extend the benchmark coverage by introducing dense tabletop objects and explicit multi-level functional relationships. This expansion introduces critical challenges involving small-scale, dense, and similar instances, with lack of visual anchoring in relational reasoning, instance confusion during cross-frame fusion, and attribution uncertainty under dynamic viewpoints. To address these issues, we propose an open-vocabulary pipeline based on 2D visual grounding and 3D graph optimization. Specifically, we anchor fine-grained functional edges from 2D visual evidence, and associate nodes across frames in 3D using multiple cues. Furthermore, edge association is formulated as temporal graph optimization, integrating evidence accumulation, entropy regularization, and temporal smoothing to robustly determine the functional connections of each node. Finally, global hierarchy shaping is performed to recover the hierarchical graph structure. Extensive experiments demonstrate that the proposed method can reliably infer functional 3D scene graphs in challenging real-world scenes, thereby further unlocking their potential for practical applications.
Related papers
- Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding [7.823301846592561]
Stream3Dv2 is a training-free framework designed for robust streaming 3D perception.<n>We introduce a comprehensive geometric-semantic fusion mechanism that resolves geometric noise and semantic ambiguity.<n>Experiments on public datasets demonstrate that Stream3Dv2 consistently outperforms existing baselines in foundational open-vocabulary streaming 3D segmentation and detection.
arXiv Detail & Related papers (2026-08-21T14:13:02Z) - OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments [4.039245878626346]
3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments.<n>We propose OP3DSG, an open-vocabulary part-aware 3DSG generation framework.<n> OP3DSG integrates object-part knowledge-guided detection with part-aware 3D fusion to preserve small and interaction-relevant components.
arXiv Detail & Related papers (2026-06-29T05:05:54Z) - FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning [48.3789545323541]
We introduce FunFact, a framework for constructing probabilistic open-vocabulary functional 3D scene graphs.<n>FunFact first builds an object- and part-centric 3D map and uses foundation models to propose semantically plausible functional relations.<n>To benchmark this setting, we introduce FunThor, a synthetic dataset based on AI2-THOR with part-level geometry and rule-based functional annotations.
arXiv Detail & Related papers (2026-04-04T11:53:12Z) - KeySG: Hierarchical Keyframe-Based 3D Scene Graphs [1.5134439544218246]
KeySG represents 3D scenes as a hierarchical graph consisting of floors, rooms, objects, and functional elements.<n>We leverage VLM to extract scene information, alleviating the need to explicitly model relationship edges between objects.<n>Our approach can process complex and ambiguous queries while mitigating the scalability issues associated with large scene graphs.
arXiv Detail & Related papers (2025-10-01T15:53:27Z) - FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction [1.8124328823188356]
We aim to develop a representation that enables robots to directly interact with their environment.<n>We focus on detecting and storing objects at a finer resolution, focusing on affordance-relevant parts.<n>We leverage currently available 3D resources to generate 2D data and train a detector, which is then used to augment the standard 3D scene graph generation pipeline.
arXiv Detail & Related papers (2025-03-10T23:13:35Z) - GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding [53.42728468191711]
Open-Vocabulary 3D object affordance grounding aims to anticipate action possibilities'' regions on 3D objects with arbitrary instructions.<n>We propose GREAT (GeometRy-intEntion collAboraTive inference) for Open-Vocabulary 3D Object Affordance Grounding.
arXiv Detail & Related papers (2024-11-29T11:23:15Z) - Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding [59.51535163599723]
FreeGS is an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene understanding without the need for 2D labels.<n>FreeGS performs comparably to state-of-the-art methods while avoiding the complex data preprocessing workload.
arXiv Detail & Related papers (2024-11-29T08:52:32Z) - Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding.
An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
arXiv Detail & Related papers (2024-11-25T10:14:10Z) - Incremental 3D Semantic Scene Graph Prediction from RGB Sequences [86.77318031029404]
We propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence.
Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network.
The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities.
arXiv Detail & Related papers (2023-05-04T11:32:16Z) - Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud
Analysis [66.49788145564004]
We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology.
Our methods perform favorably against the current state-of-the-art competitors.
arXiv Detail & Related papers (2022-12-17T15:05:25Z) - Hierarchical Graph Networks for 3D Human Pose Estimation [50.600944798627786]
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
We argue that this skeletal topology is too sparse to reflect the body structure and suffer from serious 2D-to-3D ambiguity problem.
We propose a novel graph convolution network architecture, Hierarchical Graph Networks, to overcome these weaknesses.
arXiv Detail & Related papers (2021-11-23T15:09:03Z) - Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph
Analysis [21.920148546359016]
We propose a 3D point-based scene graph generation framework to bridge perception and reasoning.
Within the reasoning stage, an EDGE-oriented Graph Convolutional Network is created to exploit multi-dimensional edge features.
Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies.
arXiv Detail & Related papers (2021-03-09T17:09:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.