Papers with Code 아카이브
Novel View Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 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/azzarelli/wape
https://github.com/bebeal/mipnerf-pytorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/blurgyy/jaxngp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/brack-wang/zeco
X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction
https://github.com/bycloudai/instant-ngp-windows
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/byeongjun-park/HarmonyView
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
https://github.com/cheind/pure-torch-ngp
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/chenguolin/DiffSplat
SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion
https://github.com/christopher-beckham/hologan-pytorch
HoloGAN: Unsupervised learning of 3D representations from natural images
https://github.com/cmhungsteve/Awesome-Transformer-Attention
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/crockwell/cap3d
https://github.com/cvg/nice-slam
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
https://github.com/drawingprocess/infofusioncontroller
NeRF-SR: High-Quality Neural Radiance Fields using Supersampling
https://github.com/drexubery/viewcrafter
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
https://github.com/dtc111111/multi-modal-uav
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
https://github.com/eigenvivek/diffdrr
Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
https://github.com/eitan3/continuous_view_synthesis_gluon
Monocular Neural Image Based Rendering with Continuous View Control
https://github.com/eitan3/nerf_gluon
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/emanuelbodin/hologan-2.0
HoloGAN: Unsupervised learning of 3D representations from natural images
https://github.com/facebookresearch/NSVF
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/facebookresearch/mvdust3r
MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds
https://github.com/facebookresearch/nonrigid_nerf
https://github.com/facebookresearch/pytorch3d
https://github.com/facebookresearch/pytorch3d/tree/master/projects/nerf
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/facebookresearch/uco3d
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
https://github.com/facebookresearch/vkraygs
https://github.com/flynn-chen/faceforensics_benchmark
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/frankhome61/nonrigid-nerf
https://github.com/futurexiang/soda
SODA: Bottleneck Diffusion Models for Representation Learning
https://github.com/glouppe/info8010-deep-learning
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/google/hypernerf
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
https://github.com/google/samurai
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
https://github.com/googleinterns/ibrnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.