On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments
- URL: http://arxiv.org/abs/2404.13842v1
- Date: Mon, 22 Apr 2024 02:42:32 GMT
- Title: On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments
- Authors: Gang Ma, Hui Wei,
- Abstract summary: In 3D scenes, rich spatial geometric and topological information are often ignored by RGB-based approaches for scene understanding.
In this study, we develop a bottom-up approach for scene understanding that infers support relations between objects from a point cloud.
- Score: 3.4535508414601344
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the years, scene understanding has attracted a growing interest in computer vision, providing the semantic and physical scene information necessary for robots to complete some particular tasks autonomously. In 3D scenes, rich spatial geometric and topological information are often ignored by RGB-based approaches for scene understanding. In this study, we develop a bottom-up approach for scene understanding that infers support relations between objects from a point cloud. Our approach utilizes the spatial topology information of the plane pairs in the scene, consisting of three major steps. 1) Detection of pairwise spatial configuration: dividing primitive pairs into local support connection and local inner connection; 2) primitive classification: a combinatorial optimization method applied to classify primitives; and 3) support relations inference and hierarchy graph construction: bottom-up support relations inference and scene hierarchy graph construction containing primitive level and object level. Through experiments, we demonstrate that the algorithm achieves excellent performance in primitive classification and support relations inference. Additionally, we show that the scene hierarchy graph contains rich geometric and topological information of objects, and it possesses great scalability for scene understanding.
Related papers
- Task-Driven Graph Attention for Hierarchical Relational Object
Navigation [25.571175038938527]
Embodied AI agents in large scenes often need to navigate to find objects.
We study a naturally emerging variant of the object navigation task, hierarchical object navigation (HRON)
We propose a solution that uses scene graphs as part of its input and integrates graph neural networks as its backbone.
arXiv Detail & Related papers (2023-06-23T19:50:48Z) - Collaborative Learning for Hand and Object Reconstruction with
Attention-guided Graph Convolution [49.10497573378427]
Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality.
Our algorithm is optimisation to object models, and it learns the physical rules governing hand-object interaction.
Experiments using four widely-used benchmarks show that our framework achieves beyond state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D hand and object shapes.
arXiv Detail & Related papers (2022-04-27T17:00:54Z) - Exploiting Scene Graphs for Human-Object Interaction Detection [81.49184987430333]
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects.
We propose a novel method to exploit this information, through the scene graph, for the Human-Object Interaction (SG2HOI) detection task.
Our method, SG2HOI, incorporates the SG information in two ways: (1) we embed a scene graph into a global context clue, serving as the scene-specific environmental context; and (2) we build a relation-aware message-passing module to gather relationships from objects' neighborhood and transfer them into interactions.
arXiv Detail & Related papers (2021-08-19T09:40:50Z) - Scenes and Surroundings: Scene Graph Generation using Relation
Transformer [13.146732454123326]
This work proposes a novel local-context aware architecture named relation transformer.
Our hierarchical multi-head attention-based approach efficiently captures contextual dependencies between objects and predicts their relationships.
In comparison to state-of-the-art approaches, we have achieved an overall mean textbf4.85% improvement.
arXiv Detail & Related papers (2021-07-12T14:22:20Z) - Learning Spatial Context with Graph Neural Network for Multi-Person Pose
Grouping [71.59494156155309]
Bottom-up approaches for image-based multi-person pose estimation consist of two stages: keypoint detection and grouping.
In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN)
The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association.
arXiv Detail & Related papers (2021-04-06T09:21:14Z) - 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) - HOSE-Net: Higher Order Structure Embedded Network for Scene Graph
Generation [20.148175528691905]
This paper presents a novel structure-aware embedding-to-classifier(SEC) module to incorporate both local and global structural information of relationships into the output space.
We also propose a hierarchical semantic aggregation(HSA) module to reduce the number of subspaces by introducing higher order structural information.
The proposed HOSE-Net achieves the state-of-the-art performance on two popular benchmarks of Visual Genome and VRD.
arXiv Detail & Related papers (2020-08-12T07:58:13Z) - Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical
Understanding of Outdoor Scene [76.4183572058063]
We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks.
The dataset has been point-wisely annotated with both hierarchical and instance-based labels.
We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
arXiv Detail & Related papers (2020-08-11T19:10:32Z) - Bidirectional Graph Reasoning Network for Panoptic Segmentation [126.06251745669107]
We introduce a Bidirectional Graph Reasoning Network (BGRNet) to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes.
BGRNet first constructs image-specific graphs in both instance and semantic segmentation branches that enable flexible reasoning at the proposal level and class level.
arXiv Detail & Related papers (2020-04-14T02:32:10Z) - Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions [94.17683799712397]
We focus on scene graphs, a data structure that organizes the entities of a scene in a graph.
We propose a learned method that regresses a scene graph from the point cloud of a scene.
We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.
arXiv Detail & Related papers (2020-04-08T12:25:25Z)
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.