ManiFold / README.md
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metadata
license: cc-by-nc-3.0
pipeline_tag: image-to-3d
tags:
  - art
  - Mathematics
  - Maths
  - SVECTOR
  - ManiFold

ManiFold

Overview

ManiFold, developed by SVECTOR, features cutting-edge AI models designed for high performance across diverse domains. It delivers scalability, efficiency, and state-of-the-art results. This document provides an in-depth guide on the capabilities of the ManiFold models and how to integrate them into your applications.


3D Construction Demo (CAR)

3D Construction Demo (TV)

Installation

To get started, ensure your environment meets these requirements:

  • Python Version: 3.8 or higher
  • Dependencies:
    • torch
    • safetensors
    • numpy

Install the dependencies using:

pip install torch safetensors numpy

Features

  • State-of-the-Art Performance: Designed for efficiency and scalability.
  • Flexible Integration: Compatible with modern machine learning frameworks.
  • Versatile Applications: Suitable for tasks ranging from image analysis to advanced AI workflows.

Example Use Cases

  1. 3D Reconstruction: Generate sparse and dense 3D models with high accuracy.
  2. Image Analysis: Leverage advanced image conditioning for enhanced visual processing.
  3. AI Workflow Integration: Streamline AI tasks with robust model capabilities.

Support

For assistance or inquiries, please contact us:


License

This project is licensed under the SVECTOR Proprietary License. Refer to the LICENSE file for more details.

license: cc-by-nc-3.0

Credits

  • Developed by: SVECTOR

Tagline: Empowering the Future with Intelligent Solutions.