Papers with Code 아카이브

NeRF

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/19reborn/pict_smoke

공식 코드

Physics-Informed Learning of Characteristic Trajectories for Smoke Reconstruction

https://github.com/ActiveVisionLab/nerfmm

공식 코드

NeRF--: Neural Radiance Fields Without Known Camera Parameters

https://github.com/ActiveVisionLab/nope-nerf

공식 코드

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

https://github.com/ActiveVisionLab/porf

공식 코드

PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction

https://github.com/AliYoussef97/SuperPoint-PrP

공식 코드

NeRF-Supervised Feature Point Detection and Description

https://github.com/Aswhalefall/VISTA

공식 코드

Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning

https://github.com/BIT-DYN/OpenObj

공식 코드

OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding

https://github.com/BayesRays/BayesRays

공식 코드

Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

https://github.com/CVLAB-Unibo/clip2nerf

공식 코드

Connecting NeRFs, Images, and Text

https://github.com/Caoang327/HexPlane

공식 코드

HexPlane: A Fast Representation for Dynamic Scenes

https://github.com/DanaCohen95/Set-the-Scene

공식 코드

Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes

https://github.com/DengKaiCQ/GigaSLAM

공식 코드

GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats

https://github.com/Factral/UnMix-NeRF

공식 코드

UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields

https://github.com/Florian-Barthel/3d-multiview-inversion

공식 코드

Multi-view Inversion for 3D-aware Generative Adversarial Networks

https://github.com/FlorisE/neural-labeling

공식 코드

NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields

https://github.com/ForMyCat/SparseGS

공식 코드

SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting

https://github.com/GATECH-EIC/Omni-Recon

공식 코드

Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields

https://github.com/GhostCai/PortraitRelighting

공식 코드

Real-time 3D-aware Portrait Video Relighting

https://github.com/GopiRajuMatta/BeSplat

공식 코드

BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream

https://github.com/Gword/Recursive-NeRF

공식 코드

Recursive-NeRF: An Efficient and Dynamically Growing NeRF

https://github.com/HITSZ-NRSL/MSI-NeRF

공식 코드

MSI-NeRF: Linking Omni-Depth with View Synthesis through Multi-Sphere Image aided Generalizable Neural Radiance Field

https://github.com/HZXu-526/Human-Avatar

공식 코드

Efficient Neural Implicit Representation for 3D Human Reconstruction

https://github.com/Ikarosy/SpikingNeRF-of-CASIA

공식 코드

SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

https://github.com/JanaldoChen/Anim-NeRF

공식 코드

Animatable Neural Radiance Fields from Monocular RGB Videos

https://github.com/JiangWenPL/FisherRF

공식 코드

FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information

https://github.com/JiatongXia/Pseudo-Labeling

공식 코드

Enhancing Close-up Novel View Synthesis via Pseudo-labeling

https://github.com/JohannaXie/GauSS-MI

공식 코드

GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstruction

https://github.com/JulianKnodt/nerf_atlas

공식 코드

Continuous Dynamic-NeRF: Spline-NeRF

https://github.com/JunzheJosephZhu/HiFA

공식 코드

HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance

https://github.com/KU-CVLAB/3DFuse

공식 코드

Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation

https://github.com/KU-CVLAB/3DGAN-Inversion

공식 코드

3D GAN Inversion with Pose Optimization

https://github.com/KU-CVLAB/CoPoNeRF

공식 코드

Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs

https://github.com/KU-CVLAB/DaRF

공식 코드

DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

https://github.com/KU-CVLAB/GeCoNeRF

공식 코드

GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency

https://github.com/KU-CVLAB/SE-NeRF

공식 코드

Self-Evolving Neural Radiance Fields

https://github.com/KU-CVLAB/Talk3D

공식 코드

Talk3D: High-Fidelity Talking Portrait Synthesis via Personalized 3D Generative Prior

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