Generating Hierarchical 3D Scene Graphs for Indoor and Outdoor Environments using Language-Enabled Spatial Ontologies

Centrala begrepp

This paper proposes a neuro-symbolic approach to extend 3D scene graph construction to arbitrary indoor and outdoor environments by leveraging Large Language Models to build a spatial ontology and using Logic Tensor Networks to incorporate the ontology during training and inference.

Sammanfattning

The paper proposes a method to build hierarchical 3D scene graphs in both indoor and outdoor environments. The key challenges addressed are:

Defining a comprehensive hierarchy of semantic concepts to describe outdoor environments, which is more complex than for indoor scenes. The authors use Large Language Models (LLMs) to automatically build a spatial ontology defining relevant concepts and relations, reducing manual effort.

Lack of training data for outdoor scene graph generation. The authors leverage Logic Tensor Networks (LTNs) to incorporate the spatial ontology as logical rules or axioms during training, reducing the need for labeled data and improving generalization.

Reliability of learning-based methods. The LTN-based approach constrains the model predictions to be consistent with the spatial ontology, improving the reliability of the 3D scene graph generation.

The authors evaluate the proposed approach on indoor (Matterport3D) and outdoor (West Point, Castle Island) datasets. The results show that the LTN-based model significantly outperforms the purely data-driven baseline, especially when training data is limited. The LTN-based model can also predict labels unseen during training by leveraging the ontology.
Overall, the paper presents a novel neuro-symbolic approach to extend 3D scene graph construction to arbitrary environments, demonstrating the benefits of combining language-enabled spatial ontologies and logical reasoning during training and inference.

Statistik

The paper does not provide specific numerical data or metrics in the text. The results are presented in the form of accuracy plots and comparisons.

Citat

"While existing work on 3D scene graphs focus on grounding concepts for indoor environments, we extend the 3D scene graph model in [2], [4] to ground concepts in arbitrary environments (e.g., indoor and outdoor)."
"To address the three challenges above, we resort to a novel learning approach, namely Logic Tensor Networks (LTNs) [11]. LTNs provide a way to add logical rules while training a neural network; these rules provide an additional supervisory signal at training time (hence reducing the amount of training data required) and constrain the network prediction at test time (hence increasing the reliability of the predictions)."

Viktiga insikter från

Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies

by Jared Strade... på arxiv.org 04-26-2024

https://arxiv.org/pdf/2312.11713.pdf

Djupare frågor

How can the proposed approach be extended to handle dynamic environments and incorporate temporal information in the 3D scene graphs?

To handle dynamic environments and incorporate temporal information in the 3D scene graphs, the proposed approach can be extended by integrating real-time sensor data and updating the scene graph dynamically. This can involve continuously updating the spatial ontology based on the changing environment and incorporating temporal relationships between objects and locations. By utilizing techniques such as recurrent neural networks (RNNs) or temporal graph neural networks, the model can capture the evolution of the scene over time and maintain a consistent representation of the dynamic environment. Additionally, incorporating motion prediction models and object tracking algorithms can help in predicting future states of the scene and updating the scene graph accordingly.

What are the potential limitations of using LLMs to generate the spatial ontology, and how can the ontology construction be further improved?

Using Large Language Models (LLMs) to generate the spatial ontology may have limitations such as bias in the training data, lack of interpretability in the generated ontology, and challenges in handling complex spatial relationships. To address these limitations and improve ontology construction, several strategies can be employed. Firstly, incorporating domain-specific knowledge and constraints during the ontology generation process can help in ensuring the relevance and accuracy of the spatial concepts and relations. Additionally, leveraging human feedback and validation mechanisms can help in refining the ontology and correcting any inaccuracies introduced by the LLM. Furthermore, exploring ensemble methods that combine multiple LLM-generated ontologies or integrating external knowledge bases can enhance the completeness and robustness of the spatial ontology.

How can the 3D scene graph representations be leveraged for high-level reasoning and task planning in robotics applications beyond navigation?

The 3D scene graph representations can be leveraged for high-level reasoning and task planning in robotics applications beyond navigation by enabling semantic understanding of the environment and facilitating complex decision-making processes. One way to achieve this is by integrating the scene graph with a knowledge base or domain-specific rules to enable logical reasoning and inference capabilities. This can support tasks such as object manipulation, object interaction, and scene understanding in unstructured environments. Additionally, the scene graph can serve as a foundation for task planning algorithms, enabling robots to autonomously perform tasks based on the spatial relationships and semantic information encoded in the graph. By incorporating reinforcement learning techniques and symbolic reasoning approaches, the scene graph can be utilized for adaptive and intelligent robotic behavior in diverse real-world scenarios.