awesome-repositories.com
Blog
MCP
awesome-repositories.com

Entdecke die besten Open-Source-Repositories mit KI-gestützter Suche.

EntdeckenKuratierte SuchenOpen-Source-AlternativenSelf-hosted SoftwareBlogSitemap
ProjektÜber unsRanking-MethodikPresseMCP-Server
RechtlichesDatenschutzAGB
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
apple avatar

apple/ml-sharp

0
View on GitHub↗
7,638 Stars·529 Forks·Python·other·4 Aufrufeapple.github.io/ml-sharp↗

Ml Sharp

ml-sharp is a neural radiance field framework designed for single-image 3D reconstruction. It uses a neural network to predict 3D geometry and appearance from a single photograph in a single feedforward pass.

The system generates metric 3D scene representations and includes a real-time view synthesizer for producing high-resolution images of new viewpoints. It also features a camera trajectory renderer that creates video sequences by moving a virtual camera through the predicted 3D space.

The project covers coordinate-based neural rendering, 3D Gaussian representation regression, and real-time 3D visualization. It enables photorealistic view synthesis and virtual camera path interpolation.

Features

  • Single-Image 3D Reconstructions - Creates a digital 3D model of a scene from a single photograph using a neural network.
  • Coordinate-Based Neural Representations - Implements coordinate-based neural representations to map spatial coordinates to scene color and density.
  • Novel View Synthesis Engines - Generates high-quality novel viewpoints from a predicted 3D representation at real-time frame rates.
  • Single-Pass Scene Generation - Generates a complete 3D scene representation from a single image using a single feedforward neural network pass.
  • Neural Radiance Field Synthesizers - Optimizes neural representations of spatial volumes to synthesize photorealistic novel views from single photographs.
  • Single View Reconstruction - Implements methods for generating 3D scene representations from a single RGB image in one pass.
  • Metric 3D Representations - Encodes geometric and appearance data into a coordinate-based format allowing for physically accurate spatial measurements.
  • Real-Time Neural Renderers - Displays complex 3D environments at interactive frame rates using neural network architectures.
  • Gaussian Primitive Regression - Predicts 3D Gaussian representations from a single photograph using a feedforward neural network.
  • Trajectory Interpolations - Produces smooth video sequences by sampling the predicted 3D representation along predefined camera coordinates.
  • Camera Trajectory Rendering - Generates sequences of images by sampling viewpoints along a predefined virtual camera path.
  • Real-Time 3D Rendering Engines - Renders 3D scenes and views at high frame rates for immediate visual feedback.

Star-Verlauf

Star-Verlauf für apple/ml-sharpStar-Verlauf für apple/ml-sharp

KI-Suche

Entdecke weitere awesome Repositories

Beschreibe in einfachen Worten, was du brauchst — die KI bewertet tausende kuratierte Open-Source-Projekte nach Relevanz.

Start searching with AI

Open-Source-Alternativen zu Ml Sharp

Ähnliche Open-Source-Projekte, sortiert nach der Anzahl der gemeinsamen Funktionen mit Ml Sharp.
  • facebookresearch/pytorch3dAvatar von facebookresearch

    facebookresearch/pytorch3d

    9,902Auf GitHub ansehen↗

    PyTorch3D is a 3D geometric deep learning library and mesh processing toolkit designed for learning from point clouds and complex 3D surface geometries. It provides a collection of reusable components and data structures for deep learning with 3D data, including a framework for training and evaluating neural radiance fields to enable photorealistic view synthesis. The project features a differentiable 3D renderer that converts meshes and point clouds into 2D images while allowing gradients to flow back into the geometry and textures. This enables 3D shape optimization, where mesh geometry, te

    Python
    Auf GitHub ansehen↗9,902
  • google-research/multinerfAvatar von google-research

    google-research/multinerf

    3,806Auf GitHub ansehen↗

    MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel views from sets of 2D images. It provides a system for generating new perspectives of a scene by optimizing a neural network based on images and camera poses. The toolkit includes research implementations such as Mip-NeRF 360 and Ref-NeRF for high-fidelity volumetric rendering. It features a structure-from-motion pipeline to calculate camera positions and orientations from image datasets to prepare data for training. The project covers a full workflow for volumetric rendering, i

    Pythonnerfneural-radiance-fields
    Auf GitHub ansehen↗3,806
  • yenchenlin/nerf-pytorchAvatar von yenchenlin

    yenchenlin/nerf-pytorch

    6,037Auf GitHub ansehen↗

    This project is a PyTorch implementation of a Neural Radiance Field framework. It serves as a 3D scene synthesizer and differentiable volumetric renderer used to train volumetric representations of scenes by predicting color and density for 3D spatial coordinates. The system enables novel view synthesis, allowing for the generation of new images of complex 3D scenes from previously unseen perspectives. It supports 3D scene reconstruction by processing 2D images and camera poses to build a digital volumetric representation of a physical space. The framework includes capabilities for 3D model

    Python
    Auf GitHub ansehen↗6,037
  • vt-vl-lab/3d-photo-inpaintingAvatar von vt-vl-lab

    vt-vl-lab/3d-photo-inpainting

    7,081Auf GitHub ansehen↗

    This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D images into 3D content by estimating monocular depth and synthesizing missing color and depth data to fill occluded regions. The system uses a layered depth image representation to manage scene boundaries and pixel connectivity. This allows for novel view synthesis, enabling the generation of videos that simulate motion parallax effects from different camera perspectives. The project covers a range of spatial modeling capabilities, including depth map estimation, disparity-based

    Python
    Auf GitHub ansehen↗7,081
Alle 30 Alternativen zu Ml Sharp anzeigen→

Häufig gestellte Fragen

Was macht apple/ml-sharp?

ml-sharp is a neural radiance field framework designed for single-image 3D reconstruction. It uses a neural network to predict 3D geometry and appearance from a single photograph in a single feedforward pass.

Was sind die Hauptfunktionen von apple/ml-sharp?

Die Hauptfunktionen von apple/ml-sharp sind: Single-Image 3D Reconstructions, Coordinate-Based Neural Representations, Novel View Synthesis Engines, Single-Pass Scene Generation, Neural Radiance Field Synthesizers, Single View Reconstruction, Metric 3D Representations, Real-Time Neural Renderers.

Welche Open-Source-Alternativen gibt es zu apple/ml-sharp?

Open-Source-Alternativen zu apple/ml-sharp sind unter anderem: facebookresearch/pytorch3d — PyTorch3D is a 3D geometric deep learning library and mesh processing toolkit designed for learning from point clouds… yenchenlin/nerf-pytorch — This project is a PyTorch implementation of a Neural Radiance Field framework. It serves as a 3D scene synthesizer and… google-research/multinerf — MultiNeRF is a 3D scene reconstruction suite and framework for training Neural Radiance Fields to synthesize novel… vt-vl-lab/3d-photo-inpainting — This project is an RGB-D image inpainting tool and framework for 3D photo reconstruction. It transforms single 2D… facebookresearch/pifuhd — pifuhd is a 3D human reconstruction framework that generates high-resolution 3D meshes of people from a single 2D… tencentarc/instantmesh — InstantMesh is a neural 3D reconstruction tool and single-image 3D mesh generator. It utilizes a sparse-view large…