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

TL;DR

This paper introduces a 3-D scene graph model for representing physical environments that enhances the accuracy, usability, and scalability of environment models for intelligent agents, demonstrated through practical applications and experiments.

Contribution

The paper proposes a novel 3-D scene graph framework that improves environment modeling for intelligent agents, addressing limitations of previous models in applicability and scalability.

Findings

Demonstrates the deployment of 3-D scene graphs in real-world applications.

Shows the framework's performance through comprehensive experiments.

Validates the model's accuracy and applicability in various conditions.

Abstract

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.