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chenhsuanlin avatar

chenhsuanlin/3D-point-cloud-generation

0
View on GitHub↗
453 stars·92 forks·Python·MIT·13 views

3D Point Cloud Generation

Chen-Hsuan Lin, Chen Kong, and Simon Lucey AAAI Conference on Artificial Intelligence (AAAI), 2018

Features

  • Generative 3D Modeling - Generates dense 3D point clouds for object reconstruction.
  • Reconstruction and Completion - Efficient point cloud generation for dense 3D reconstruction.

Star history

Star history chart for chenhsuanlin/3d-point-cloud-generationStar history chart for chenhsuanlin/3d-point-cloud-generation

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with 3D Point Cloud Generation

These projects share indexed features with 3D Point Cloud Generation. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • threestudio-project/threestudiothreestudio-project avatar

    threestudio-project/threestudio

    7,027View on GitHub↗

    Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images. It provides specialized pipelines for text-to-3D generation and image-to-3D reconstruction, utilizing a neural radiance field trainer to produce geometry and textures. The framework is distinguished by its support for hybrid geometry backends, including signed distance functions, tetrahedra grids, and volume grids. It employs score distillation sampling to guide the generation process and features a modular plugin system for loading custom modules and nodes. The system covers

    Jupyter Notebook
    View on GitHub↗7,027
  • openai/shap-eopenai avatar

    openai/shap-e

    12,251View on GitHub↗

    Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language descriptions or two-dimensional images. It functions as a generative model capable of producing three-dimensional implicit functions and assets. The project includes a 3D latent encoder that converts trimeshes and 3D models into latent representations using point clouds and multiview renders. It utilizes an image-to-3D generator to produce assets from synthetic view images and a text-to-3D generator to build shapes from text prompts. The system implements a pipeline involving latent

    Python
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  • microsoft/trellismicrosoft avatar

    microsoft/TRELLIS

    12,977View on GitHub↗

    TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions or reference images into textured 3D assets. It operates as a text-to-3D asset generator that utilizes structured latent representations to produce high-quality 3D meshes, Gaussians, and Radiance Fields. The system functions as a multi-format 3D decoder, converting internal representations into standard exchange formats such as GLB and PLY. It also serves as a 3D asset editing tool, enabling the modification of specific regions of generated objects through targeted text or im

    Python3d3d-aigc3d-generation
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  • lightningpixel/modlylightningpixel avatar

    lightningpixel/modly

    4,140View on GitHub↗

    Modly is a local AI 3D model generator that converts two-dimensional images into three-dimensional meshes. It is a privacy-focused tool that processes data directly on the host graphics card using GPU-accelerated inference. The system serves as an extensible AI model framework, allowing the integration of external model extensions and runtime files from remote repositories. It utilizes a manifest-driven plugin architecture to add new generation methods by loading metadata and files from external version control systems. The toolset includes a command-line interface for triggering generation

    TypeScript
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Compare all 30 related projects→

Frequently asked questions

What does chenhsuanlin/3d-point-cloud-generation do?

Chen-Hsuan Lin, Chen Kong, and Simon Lucey AAAI Conference on Artificial Intelligence (AAAI), 2018

What are the main features of chenhsuanlin/3d-point-cloud-generation?

The main features of chenhsuanlin/3d-point-cloud-generation are: Generative 3D Modeling, Reconstruction and Completion.

Which projects share features with chenhsuanlin/3d-point-cloud-generation?

Projects with overlapping indexed features include: openai/shap-e — Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language… threestudio-project/threestudio — Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images.… nv-tlabs/get3d — GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and… lightningpixel/modly — Modly is a local AI 3D model generator that converts two-dimensional images into three-dimensional meshes. It is a… microsoft/trellis — TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions… coplaydev/unity-mcp — Unity MCP is a plugin that connects the Unity Editor to AI assistants through the Model Context Protocol, enabling…