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

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectServer MCPDespreCum realizăm clasamentulPresă
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
F

fraunhoferhhi/D-NeuS

0
View on GitHub↗
0 stele·0 fork-uri·1 vizualizare

D NeuS

Recovering Fine Details for Neural Implicit Surface Reconstruction (WACV2023)

Features

  • Neural Surface Reconstruction - Recovers fine geometric details for neural implicit surfaces.

Istoric stele

Graficul istoricului de stele pentru fraunhoferhhi/d-neusGraficul istoricului de stele pentru fraunhoferhhi/d-neus

Căutare AI

Explorează mai multe repository-uri excelente

Descrie ce ai nevoie în limbaj simplu — AI-ul sortează mii de proiecte open source selectate în funcție de relevanță.

Start searching with AI

Alternative open-source pentru D NeuS

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu D NeuS.
  • nvlabs/neuralangeloAvatar NVlabs

    NVlabs/neuralangelo

    4,602Vezi pe GitHub↗

    Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and multi-view photography into high-fidelity 3D meshes. It implements a pipeline for training neural radiance fields to represent complex scenes as digital geometry. The project utilizes a signed distance function for surface representation and multi-resolution hash encoding to capture both coarse and fine geometric details. It employs differentiable volume rendering and gradient-based eikonal regularization to ensure the learned distance functions remain physically plausible. The syste

    Python3d-reconstructioncomputer-graphicscomputer-vision
    Vezi pe GitHub↗4,602
  • jzhangbs/mvsdfAvatar jzhangbs

    jzhangbs/MVSDF

    141Vezi pe GitHub↗

    This is the official implementation for the ICCV 2021 paper Learning Signed Distance Field for Multi-view Surface Reconstruction

    Python
    Vezi pe GitHub↗141
  • mabaorui/towardsbettergradientAvatar mabaorui

    mabaorui/TowardsBetterGradient

    53Vezi pe GitHub↗

    This repository contains the code to reproduce the results from the paper. Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment.

    Python
    Vezi pe GitHub↗53
  • ghixu/geo-neusAvatar GhiXu

    GhiXu/Geo-Neus

    310Vezi pe GitHub↗

    This is the official repo for the implementation of Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction, Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao (* Equal Contribution), NeurIPS 2022. We will release our pretrained models soon!

    Python
    Vezi pe GitHub↗310
Vezi toate cele 7 alternative pentru D NeuS→

Întrebări frecvente

Ce face fraunhoferhhi/d-neus?

Recovering Fine Details for Neural Implicit Surface Reconstruction (WACV2023)

Care sunt principalele funcționalități ale fraunhoferhhi/d-neus?

Principalele funcționalități ale fraunhoferhhi/d-neus sunt: Neural Surface Reconstruction.

Care sunt câteva alternative open-source pentru fraunhoferhhi/d-neus?

Alternativele open-source pentru fraunhoferhhi/d-neus includ: nvlabs/neuralangelo — Neuralangelo is a neural surface reconstruction framework that transforms two-dimensional image sequences and… jzhangbs/mvsdf — This is the official implementation for the ICCV 2021 paper Learning Signed Distance Field for Multi-view Surface… mabaorui/towardsbettergradient — This repository contains the code to reproduce the results from the paper. Towards Better Gradient Consistency for… ghixu/geo-neus — This is the official repo for the implementation of Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning… ventusff/neurecon — Multi-view 3D reconstruction using neural rendering. Unofficial implementation of UNISURF, VolSDF, NeuS and more. wutong16/voxurf — Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction.