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mabaorui avatar

mabaorui/NeuralPull

0
View on GitHub↗
181 estrellas·23 forks·Python·MIT·3 vistas

NeuralPull

This repository contains the code to reproduce the results from the paper.

Features

  • Generative 3D Modeling - Learns signed distance functions from point clouds.
  • Point Cloud Reconstruction - Learns signed distance functions by pulling space onto surfaces.

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Preguntas frecuentes

¿Qué hace mabaorui/neuralpull?

This repository contains the code to reproduce the results from the paper.

¿Cuáles son las características principales de mabaorui/neuralpull?

Las características principales de mabaorui/neuralpull son: Generative 3D Modeling, Point Cloud Reconstruction.

¿Qué alternativas de código abierto existen para mabaorui/neuralpull?

Las alternativas de código abierto para mabaorui/neuralpull incluyen: nv-tlabs/get3d — GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and… openai/point-e — Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language… valeoai/poco — by: Alexandre Boulch and Renaud Marlet. andy97/deepmls — This repository contains the implementation of the paper:. colin97/point2mesh — Codes for Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020). [paper]. openai/shap-e — Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language…

Alternativas open-source a NeuralPull

Proyectos open-source similares, clasificados según cuántas características comparten con NeuralPull.
  • nv-tlabs/get3dAvatar de nv-tlabs

    nv-tlabs/GET3D

    4,441Ver en GitHub↗

    GET3D is a generative 3D mesh model and rendering framework designed to synthesize high-quality textured shapes and tetrahedral meshes. It functions as an image-to-3D reconstructor and text-to-3D generator, utilizing a differentiable 3D renderer to produce realistic visual perspectives and material effects. The system enables the creation of 3D assets from single 2D images, point clouds, or descriptive text prompts. It features a latent space interpolator for creating smooth transitions between different 3D objects and supports the independent control of geometry and texture. The project cov

    Python
    Ver en GitHub↗4,441
  • openai/point-eAvatar de openai

    openai/point-e

    6,886Ver en GitHub↗

    Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove

    Python
    Ver en GitHub↗6,886
  • andy97/deepmlsAvatar de Andy97

    Andy97/DeepMLS

    137Ver en GitHub↗

    This repository contains the implementation of the paper:

    Python
    Ver en GitHub↗137
  • colin97/point2meshAvatar de Colin97

    Colin97/Point2Mesh

    98Ver en GitHub↗

    Codes for Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020). paper

    C++
    Ver en GitHub↗98
Ver las 30 alternativas a NeuralPull→