https://github.com/naufalso/keras_nerf
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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/nerfstudio-project/nerfstudio
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/niki-amini-naieni/instantcalibration
Instant Uncertainty Calibration of NeRFs Using a Meta-Calibrator
https://github.com/nvidiagameworks/kaolin-wisp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/omniobject3d/omniobject3d
https://github.com/pfnet-research/distilled-feature-fields
https://github.com/postech-cvlab/scnerf
NeRF--: Neural Radiance Fields Without Known Camera Parameters
https://github.com/prajwalsingh/NeRF-Pytorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/primecai/pix2nerf
Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation
https://github.com/princeton-vl/Princeton365
https://github.com/raymondwang987/fmnet
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
https://github.com/rover-xingyu/Ha-NeRF
https://github.com/salykovaa/instant-DexNerf
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/sanowar-raihan/meta-neus
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
https://github.com/sap3d/Sap3D
https://github.com/scannetpp/scannetpp
https://github.com/seominseok0429/Implicit-Stacked-Autoregressive-Model-for-Video-Prediction
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/shaohua0116/Multiview2Novelview
https://github.com/sillsill777/NeRF-PyTorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/silverbottlep/tvsn
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
https://github.com/simpleig/Geo-PIFu
https://github.com/sjb3d/descent/tree/main/examples/image_fit
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/songrise/HyperNeRF-torch
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
https://github.com/stelzner/srt
https://github.com/suoxinkey/PlenOctrees_NeRF-SH
PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/svip-lab/impersonator
https://github.com/sxyu/volrend
PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/tanqianq/enhance-nerf
Nerfstudio: A Modular Framework for Neural Radiance Field Development
https://github.com/theycallmepeter/pytorch3d_PBR
https://github.com/urbanarchitect/urbanarchitect
Urban Radiance Field Representation with Deformable Neural Mesh Primitives
https://github.com/ut-vision/rot-mvgaze
Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation
https://github.com/v715/DiffDRR
Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
https://github.com/vccimaging/intratomo
https://github.com/ventusff/improved-nerfmm
NeRF--: Neural Radiance Fields Without Known Camera Parameters
https://github.com/ventusff/neurecon
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
https://github.com/vishwa91/wire
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.