# Scene Representation in Artificial Intelligence
Scene representation is the process of capturing and encoding information about a visual scene in computer vision, artificial intelligence, or graphics. It involves creating a structured representation of the elements and attributes present in a scene, such as objects, their positions, sizes, colors, and relationships. This is essential for robots to navigate and understand their environment.
To meet the requirements of scalability and efficiency, researchers from the University of Toronto, MIT, and the University of Montreal have proposed a method called ConceptGraphs. This 3D scene representation method is designed for robot perception and planning. Unlike traditional methods that rely on internet-scale training data, ConceptGraphs can handle new objects and concepts during inference. It allows for flexible planning across various tasks, including collecting geometric and semantic information.
The method developed by the team uses graph structures with node representations to efficiently describe scenes. It can dynamically update the scene representations and build hierarchical 3D scene representations in real time. ConceptGraphs integrate both geometric and semantic data, making it an object-centric mapping system that connects the 3D world to 2D representations produced by image and language foundation models.
The researchers conducted experiments using real-world wheeled and legged robotic platforms and demonstrated that ConceptGraphs can perform task planning based on abstract language queries. By utilizing RGB-D frames and segmentation models, ConceptGraphs can identify candidate objects, associate them across multiple views, and instantiate nodes in a 3D scene graph. The model utilizes an LVLM to caption each node and an LLM to infer relationships between nodes and build edges in the scene graph.
Future work for this research includes incorporating temporal dynamics and testing the model in less structured environments. The ConceptGraphs model addresses key limitations in existing dense and implicit representations.
To learn more about this research, you can check out the Paper, GitHub, and Project. Join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter for the latest AI research news and cool AI projects.
If you enjoy our work, you’ll love our newsletter. Sign up now!
(Links omitted for simplicity)