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NeRF

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MindSpore-scientific/code-2/tree/main/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

https://github.com/NJU-3DV/Gaussian-Flow

Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle

https://github.com/PJLab-ADG/neuralsim/blob/main/docs/methods/neus_in_10_minutes.md

NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

https://github.com/SuLvXiangXin/zipnerf-pytorch

Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

https://github.com/SusungHong/IF-DreamFusion

DreamFusion: Text-to-3D using 2D Diffusion

https://github.com/airalcorn2/pytorch-nerf

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/albertpumarola/D-NeRF

D-NeRF: Neural Radiance Fields for Dynamic Scenes

https://github.com/aluo-x/learning_neural_acoustic_fields

AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis

https://github.com/apple/ml-gaudi

NeRF-VAE: A Geometry Aware 3D Scene Generative Model

https://github.com/ashawkey/RAD-NeRF

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

https://github.com/ashawkey/stable-dreamfusion

DreamFusion: Text-to-3D using 2D Diffusion

https://github.com/ashawkey/torch-ngp

CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields

https://github.com/astra-vision/PaSCo

SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

https://github.com/autonomousvision/factor-fields

Factor Fields: A Unified Framework for Neural Fields and Beyond

https://github.com/ayaanzhaque/instruct-nerf2nerf

Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

https://github.com/bebeal/mipnerf-pytorch

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/chenguolin/DiffSplat

Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

https://github.com/chenshuang-zhang/imagenet_d

Nerfies: Deformable Neural Radiance Fields

https://github.com/chinhsuanwu/dreamfusionacc

DreamFusion: Text-to-3D using 2D Diffusion

https://github.com/cmhungsteve/Awesome-Transformer-Attention

Structure-Aware Sparse-View X-ray 3D Reconstruction

https://github.com/code-implementation1/Code4/tree/main/lenet

LERF: Language Embedded Radiance Fields

https://github.com/code-implementation1/Code4/tree/main/lerf

LERF: Language Embedded Radiance Fields

https://github.com/codestella/putting-nerf-on-a-diet

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis

https://github.com/computational-imaging/automatic-integration

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/cvg/nice-slam

NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

https://github.com/cvlab-columbia/zero123

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors

https://github.com/darlinghang/st-nerf

Editable Free-viewpoint Video Using a Layered Neural Representation

https://github.com/drawingprocess/infofusioncontroller

NeRF-SR: High-Quality Neural Radiance Fields using Supersampling

https://github.com/dvlab-research/LargeScaleNeRFPytorch

Block-NeRF: Scalable Large Scene Neural View Synthesis

https://github.com/eitan3/nerf_gluon

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/facebookresearch/eyefultower

VR-NeRF: High-Fidelity Virtualized Walkable Spaces

https://github.com/facebookresearch/multiface

Learning Compositional Radiance Fields of Dynamic Human Heads

https://github.com/facebookresearch/nonrigid_nerf

Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

https://github.com/facebookresearch/pytorch3d/tree/master/projects/nerf

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

https://github.com/frankhome61/nonrigid-nerf

Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

https://github.com/fuxiao0719/panopticnerf

Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.