SpatialLM 1.5 - A spatial language model developed by Clustercore Technology

What is SpatialLM 1.5?

SpatialLM 1.5 is a powerful spatial language model developed by Qunhe Technology. Trained on a large language model, it understands natural language commands and outputs spatial language containing spatial structure, object relationships, and physical parameters. Users can generate structured 3D scenes using simple text descriptions through the SpatialLM-Chat dialogue system. The model can then respond to questions or edit existing scenes. For example, inputting "generate a living room suitable for the elderly" will allow the model to intelligently match furniture models, complete the layout, and add details such as non-slip handrails. SpatialLM 1.5 can be used in interior design and provides interactive scene information for tasks such as robot path planning, helping to solve the challenge of robot training data.

Main functions of SpatialLM 1.5

• Natural Language Understanding and InteractionThe model can understand natural language commands input by the user and supports generating corresponding 3D scenes based on the commands.
• Structured scene generationIt supports outputting a "spatial language" that includes spatial structure, object relationships, and physical parameters to generate structured 3D scenes, and supports parametric scene generation and editing.
• Scene Q&A and EditingUsers can ask and answer questions or edit the generated scene using natural language, such as asking "How many doors are there in the living room?" or requesting "Add a decorative painting to the wall".
• Robot training supportThe generated scenes are rich in physically accurate structured information, which can be used for robot path planning, obstacle avoidance training and task execution, solving the problem of insufficient robot training data.

Technical principles of SpatialLM 1.5

• Augmentation based on large language modelsBased on large language models such as GPT, an enhanced model is built by integrating 3D spatial description language capabilities. This model can understand natural language and use a programming language-like approach to understand, reason about, and edit indoor scenes.
• Structured outputThe model outputs a "spatial language" containing information such as spatial structure, object relationships, and physical parameters. It supports parametric scene generation and editing, providing necessary interactive scene information for tasks such as robot path planning.
• Dialogue interaction systemBased on the SpatialLM-Chat dialogue interaction system, users can easily interact with the model to realize scene generation, editing and question-and-answer functions.

Application scenarios of SpatialLM 1.5

• Interior design and decorationIt generates interior design solutions suitable for different needs based on user descriptions, such as rooms for the elderly and children. It supports real-time editing and optimization, improving design efficiency and user experience.
• Robot Training and SimulationThe structured 3D scenes generated by the model are rich in physical parameter information, which can be used for robot path planning, obstacle avoidance training, etc., to solve the problem of insufficient data in robot training and improve training effect.
• Virtual Reality (VR) and Augmented Reality (AR)It can quickly generate 3D scenes in virtual environments, providing immersive interactive experiences for VR and AR applications, such as virtual museums and virtual classrooms.
• Architectural Design and PlanningThe model can generate detailed 3D scenes of the building's interior, helping architects and planners to better showcase their designs, conduct virtual walkthroughs and effect assessments, and identify and resolve problems in advance.
• Education and TrainingIt generates virtual historical scenes, science laboratories, etc., for use in immersive learning in education and training, enhancing the fun and interactivity of learning and improving teaching effectiveness.