SpatialLM - An open-source spatial understanding multimodal model from Clustercore Technology

What is SpatialLM?

SpatialLM is an open-source spatial understanding multimodal model from GroupCore Technology, endowing robots and intelligent systems with human-like spatial cognitive abilities. By analyzing videos taken with ordinary mobile phones, it can reconstruct detailed 3D scene layouts, annotating information such as room structure, furniture placement, and passage width. Based on a large language model framework, and combining point cloud reconstruction and structured representation techniques, the model transforms scenes in videos into structured 3D models, providing an efficient foundational framework for embodied intelligence training.

Main functions of SpatialLM

• Video to 3D scene generationSpatialLM can convert videos shot with a regular mobile phone into detailed 3D scene layouts. By analyzing each frame of the video, it reconstructs the 3D structure of the scene, including information such as room layout, furniture placement, and passageway width.
• Spatial cognition and reasoningThe model breaks through the limitations of traditional large language models in understanding the geometric and spatial relationships of the physical world, endowing machines with human-like spatial cognition and analytical capabilities. It can perform semantic understanding of objects in a scene, generate structured 3D scene layouts, and annotate the three-dimensional coordinates, size parameters, and category information of objects.
• Low-cost data acquisitionSpatialLM does not require complex sensors or smart wearable devices; videos captured by ordinary mobile phones or cameras can be used as data input. This significantly lowers the data collection threshold for developers, enabling more companies and researchers to quickly conduct related research.
• Embodied Intelligence TrainingSpatialLM provides a foundational spatial understanding training framework for the field of embodied intelligence. Enterprises can fine-tune the model for specific scenarios, enabling robots to navigate, avoid obstacles, and perform tasks in complex environments. Combined with SpatialVerse, the spatial intelligence training platform from Quncore Technology, robots can learn skills in a simulated environment, forming a complete closed loop from cognition to action.
• Virtual scene generationSpatialLM can transform real-world data into rich scenes in virtual environments. Through its synthetic data engine, it can generalize and generate billions of new scenes, providing powerful support for fields such as virtual reality, augmented reality, and game development.

Technical Principles of SpatialLM

• Video Input and Point Cloud ReconstructionSpatialLM uses MASt3R-SLAM technology to process the input RGB video. It breaks the video down into frames, extracts detailed spatial points of objects, calculates their depth and position, and generates a high-density 3D point cloud model.
• Point cloud encoding and feature extractionPoint cloud data is transformed into compact feature vectors by the encoder, preserving key geometric and semantic information of objects in the scene.
• Large language model generates scene code: Using a Large Language Model (LLM), point cloud features are transformed into structured scene code. This includes the coordinates and dimensions of the spatial structure, and the semantic bounding boxes of objects are annotated (e.g., "Sofa - 1.8 meters long - 0.5 meters from the wall").
• Structured 3D layout generationThe scene code is further converted into a structured 3D scene layout, clearly annotating the 3D coordinates, size parameters, and category information of each object. This can be reconstructed into an interactive 3D scene using visualization tools.
• Physical rule embeddingSpatialLM incorporates physical principles (such as "furniture cannot be suspended in the air" and "passage width ≥ 0.8 meters") to ensure that the generated 3D scenes conform to physical rules.

SpatialLM project address

• Github repository:https://github.com/manycore-research/SpatialLM
• HuggingFace model library:https://huggingface.co/manycore-research/SpatialLM-Llama-1B

Application scenarios of SpatialLM

• Embodied Intelligence TrainingSpatialLM can help robots train for tasks such as obstacle avoidance and object grasping in a virtual environment.
• Automatic navigationIn robot navigation tasks, SpatialLM can analyze spatial information in the environment in real time, helping the robot avoid obstacles and plan the optimal path.
• AR/VR fieldSpatialLM can quickly transform real-world scenes into rich scenes in a virtual environment, supporting the development of augmented reality and virtual reality applications.
• Architectural Design and PlanningSpatialLM can analyze 3D point cloud data of buildings and identify structural information such as walls, doors and windows, helping architectural designers and planners to carry out design work more efficiently.
• Education and TrainingEducators can use SpatialLM to develop educational software that helps students learn 3D modeling and spatial vision skills. Through intuitive 3D scene generation and parsing, students can better understand spatial relationships and geometric concepts.