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/facebookresearch/KeypointNeRF
공식 코드https://github.com/facebookresearch/NeuralPassthrough
공식 코드https://github.com/facebookresearch/TemporallyConsistentDepth
공식 코드Temporally Consistent Online Depth Estimation Using Point-Based Fusion
https://github.com/facebookresearch/hyperreel
공식 코드HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
https://github.com/facebookresearch/multiface
공식 코드https://github.com/facebookresearch/replay_dataset
공식 코드Replay: Multi-modal Multi-view Acted Videos for Casual Holography
https://github.com/facebookresearch/synsin
공식 코드https://github.com/facebookresearch/vggt
공식 코드https://github.com/facebookresearch/worldsheet
공식 코드Worldsheet: Wrapping the World in a 3D Sheet for View Synthesis from a Single Image
https://github.com/fangli333/sc-4dgs
공식 코드Self-Calibrating 4D Novel View Synthesis from Monocular Videos Using Gaussian Splatting
https://github.com/felixcheng97/agap
공식 코드Learning Naturally Aggregated Appearance for Efficient 3D Editing
https://github.com/fictionarry/dngaussian
공식 코드DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
https://github.com/fraunhofer-iis/livenvs
공식 코드https://github.com/fraunhoferhhi/Self-Organizing-Gaussians
공식 코드Compact 3D Scene Representation via Self-Organizing Gaussian Grids
https://github.com/fraunhoferhhi/improving-adc-3dgs
공식 코드Improving Adaptive Density Control for 3D Gaussian Splatting
https://github.com/freedomcls/nerf-vpt
공식 코드NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning
https://github.com/fudan-zvg/efficient4d
공식 코드Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
https://github.com/fudan-zvg/gs-lidar
공식 코드GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting
https://github.com/fudan-zvg/nerf-lidar
공식 코드NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
https://github.com/fudan-zvg/realengine
공식 코드RealEngine: Simulating Autonomous Driving in Realistic Context
https://github.com/fukunyin/coco-nerf
공식 코드Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations
https://github.com/gaetan-landreau/epipolar_NVS
공식 코드EpipolarNVS: leveraging on Epipolar geometry for single-image Novel View Synthesis
https://github.com/ganwanshui/v4d
공식 코드https://github.com/gap-lab-cuhk-sz/gaustudio
공식 코드GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
https://github.com/gapszju/3DGS-DR
공식 코드https://github.com/gatech-eic/nerfool
공식 코드https://github.com/gaurav00700/selfsupervised-nvsf
공식 코드https://github.com/generalizable-neural-performer/gnr
공식 코드Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis
https://github.com/georghess/neurad-studio
공식 코드https://github.com/gerwang/shadowneus
공식 코드ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision
https://github.com/giantai/pixtrack
공식 코드PixTrack: Precise 6DoF Object Pose Tracking using NeRF Templates and Feature-metric Alignment
https://github.com/gjgjgjfff/AG-NeRF
공식 코드https://github.com/gkouros/ref-dvgo
공식 코드https://github.com/gohyojun15/SplatFlow
공식 코드SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis
https://github.com/google-research/google-research/tree/master/light_field_neural_rendering
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.