Point·E is an open-source system for generating 3D point clouds from complex prompts, targeting researchers and developers in 3D content creation. It offers a novel approach to 3D synthesis, enabling the creation of point cloud models from text or image inputs.

How It Works

Point·E utilizes a diffusion model to generate 3D point clouds. It first generates a coarse point cloud from a prompt, then upsamples it to a higher resolution. This two-stage diffusion process allows for efficient generation of detailed 3D representations.

Quick Start & Requirements

• Install with pip install -e ..
• Requires Python.
• Notebooks for examples: image2pointcloud.ipynb, text2pointcloud.ipynb, pointcloud2mesh.ipynb.
• Evaluation scripts: evaluate_pfid.py, evaluate_pis.py.
• Blender rendering code: blender_script.py.
• Sample data available for download.

Highlighted Details

• Generates 3D point clouds from text or image prompts.
• Includes a mesh generation capability via SDF regression.
• Provides evaluation scripts for P-FID and P-IS metrics.
• Includes Blender rendering scripts for visualization.

Maintenance & Community

This is an official code and model release from OpenAI. Further community engagement details are not provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The text-to-3D model's capabilities are noted as limited, understanding only simple categories and colors.