Papers with Code 아카이브
Novel View Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 24
- 보존된 평가 기록
- 103
Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.
See Wiki for more introductions.
The Synthesis method include: NeRF, MPI and so on.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CompVis/image2video-synthesis-using-cINNs
https://github.com/DL3DV-10K/Dataset
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
https://github.com/DavidLandup0/deepvision
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/Fyusion/LLFF
Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines
https://github.com/HengyiWang/neural-rgbd-torch
https://github.com/JhihYangWu/UnofficialGeNVS
Generative Novel View Synthesis with 3D-Aware Diffusion Models
https://github.com/Jittor/JNeRF
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/JonathonLuiten/instant-nerf-pytorch
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/JulianKnodt/nerf_atlas
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/Leminhbinh0209/AAAI22-ADD
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/Linyou/taichi-ngp-renderer
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/Madaoer/S3IM-Neural-Fields
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/FreeNeRF_Improving_Few_shot_Neural_Rendering_with_Free_Frequency_Regularization
FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/InfoNeRF_Ray_Entropy_Minimization_for_Few_Shot_Neural_Volume_Rendering
InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Instant_Neural_Graphics_Primitives_with_a_Multiresolution_Hash_Encoding
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/KPlanes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/NeRF_Representing_Scenes_as_Neural_Radiance_Fields_for_View_Synthesis
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Neural_Radiance_Fields_Without_Known_Camera_Parameters
NeRF--: Neural Radiance Fields Without Known Camera Parameters
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/PlenOctrees_for_Real_time_Rendering_of_Neural_Radiance_Fields
PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/OpenSpaceAI/TI-Face
TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
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/SSRSGJYD/NeuralTexture
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/ShanghaiTech-IMPACT/4DRGS
TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction
https://github.com/SuLvXiangXin/zipnerf-pytorch
https://github.com/VladimirYugay/MAGiC-SLAM
https://github.com/YoungJoongUNC/Neural_Human_Performer
https://github.com/airalcorn2/pytorch-nerf
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/allenai/objaverse-xl
https://github.com/andrewjong/Global-Flow-Local-Attention-VTryon
https://github.com/apchenstu/mvsnerf
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/gaussian-opacity-fields
Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
https://github.com/autonomousvision/lara
https://github.com/autonomousvision/mip-splatting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.