joaquinix

Toolkit: Benchmarking 3D and 4D World Models in the Real World 🌍

Table of Contents

• Overview
• Features
• Installation
• Usage
• Topics
• Contributing
• License
• Contact

Overview

The Toolkit repository focuses on benchmarking 3D and 4D world models. This project aims to provide researchers and developers with tools to evaluate the performance of various world models in real-world scenarios. The toolkit supports a variety of tasks, including 3D generation, 4D generation, and video generation, making it a versatile resource for spatial intelligence and embodied AI.

Features

• 3D Generation: Create realistic 3D models using advanced algorithms.
• 4D Generation: Explore time-based models that incorporate dynamic changes.
• LiDAR Generation: Generate LiDAR data for accurate spatial mapping.
• Occupancy Generation: Create occupancy grids for navigation and planning.
• Spatial Intelligence: Enhance AI’s understanding of physical spaces.
• Video Generation: Generate video outputs from 3D models.
• AIGC Support: Integrate with AIGC frameworks for advanced capabilities.

Installation

To get started with Toolkit, follow these steps:

1. Clone the Repository:

   git clone https://github.com/joaquinix/toolkit.git
   cd toolkit
   

2. Install Dependencies: Ensure you have Python 3.8 or higher installed. Use pip to install the required packages:

   pip install -r requirements.txt
   

3. Download Releases: You can find the latest releases here. Download the necessary files and execute them to get started.

Usage

After installation, you can start using the toolkit. Here’s a simple example to generate a 3D model:

from toolkit import ModelGenerator

generator = ModelGenerator()
model = generator.create_3d_model(parameters)
model.save('output_model.obj')

Command-Line Interface

The toolkit also provides a command-line interface (CLI) for quick operations. You can run the following command to generate a 4D model:

python cli.py generate_4d --params "your_parameters_here"

Topics

This repository covers a range of topics relevant to modern AI and model generation:

• 3D Generation: Techniques and algorithms for creating three-dimensional models.
• 4D Generation: Understanding the fourth dimension in modeling.
• AIGC: Tools for integrating AI-generated content.
• Embodied AI: Concepts and implementations for AI that interacts with the physical world.
• LiDAR Generation: Methods for simulating LiDAR data for spatial analysis.
• Occupancy Generation: Techniques for generating occupancy maps for navigation.
• Spatial Intelligence: Understanding and modeling the spatial relationships in environments.
• Video Generation: Creating dynamic video content from static models.
• World Models: Evaluating and benchmarking various world models in real-world applications.

Contributing

We welcome contributions from the community. To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

Please ensure your code adheres to the project’s coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or support, please reach out via:

• Email: your-email@example.com
• GitHub Issues: Submit an Issue

Explore more and stay updated with our latest developments by visiting our Releases section.