SpatialLM: A Powerful Tool for 3D Space Mapping

SpatialLM is an impressive AI tool that can analyze a video, generate a 3D map of a space, and identify key structural elements such as walls, doors, windows, and furniture. This technology has broad applications, including architecture, interior design, autonomous driving, and surveillance.

SpatialLM Overview:

Detail	Description
Name	SpatialLM
GitHub Repository	SpatialLM GitHub Code
Project Page	SpatialLM Project Page
HuggingFace Demo	Try SpatialLM on Hugging Face

How SpatialLM Works

Video Analysis and 3D Mapping

SpatialLM processes input videos and creates a 3D point cloud representation of the environment. It identifies and labels various objects within the space, ensuring that the spatial relationships between these objects remain consistent, even as the viewpoint changes. This allows for a precise and accurate structural representation of the environment.

Master SLAM and Point Cloud Encoding

The system relies on a technique called Master SLAM (Simultaneous Localization and Mapping) to generate a 3D point cloud of the video. The point cloud is then compressed using a specialized point cloud encoder, making it more efficient for further processing.

Large Language Model Integration

The compressed data is fed into a large language model, which generates the 3D structural layout of the space. The output can be expressed in multiple formats:

• A detailed structural dataset
• A 2D floor plan
• Industry-standard formats for professional use

Installation

Follow these steps to install SpatialLM:

Tested Environment

• Python 3.11
• Pytorch 2.4.1
• CUDA Version 12.4

Steps

1. Clone the Repository

   git clone https://github.com/manycore-research/SpatialLM.git
   cd SpatialLM

2. Create a Conda Environment with CUDA 12.4

   conda create -n spatiallm python=3.11
   conda activate spatiallm
   conda install -y nvidia/label/cuda-12.4.0::cuda-toolkit conda-forge::sparsehash

3. Install Dependencies with Poetry

   pip install poetry && poetry config virtualenvs.create false --local
   poetry install
   poe install-torchsparse # Building wheel for torchsparse will take a while

Inference

In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.

Applications of SpatialLM

Interior Design and Architecture

SpatialLM allows architects and designers to quickly map out spaces and optimize layouts. For example, if a user wants to change a bed to a king-size model, the tool detects that the desk and chair will no longer fit and automatically adjusts the layout accordingly.

Robotics and Intelligent Assistants

The technology enables robots to interact intelligently with their surroundings. For instance, a robot can ask, "I just cleaned the kitchen. How do I go to the bedroom to set up the bed?" Using its spatial awareness and floor plan knowledge, SpatialLM can provide step-by-step navigation instructions.

Enhanced Human Interaction

SpatialLM can function as an intelligent assistant capable of answering spatial queries. If a user provides a video of a bedroom, the AI can reconstruct the layout and suggest modifications, making it a valuable tool for both professionals and everyday users.

Running SpatialLM Locally

GitHub Repository and Installation

The developers have made the code publicly available on GitHub, including detailed installation instructions. Users can easily download and run the model on a local machine.

Lightweight Model Options

Two versions of SpatialLM are available:

• Llama (1 billion parameters)
• Quen (0.5 billion parameters)

Both models are lightweight and can be run on a consumer-grade GPU, making them accessible for a wide range of users.

Conclusion

SpatialLM is an incredibly useful tool for various industries, providing real-time 3D space mapping and intelligent spatial awareness. It is used for architecture, interior design, robotics, or human interaction, this AI model offers an efficient way to analyze and interact with physical spaces.

With its open-source availability and ease of installation, SpatialLM has the potential to become a widely adopted tool in multiple fields.