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/sxyu/pixel-nerf
공식 코드https://github.com/sxyu/plenoctree
공식 코드PlenOctrees for Real-time Rendering of Neural Radiance Fields
https://github.com/synthesiaresearch/humanrf
공식 코드HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
https://github.com/syxieee/supergs
공식 코드https://github.com/taekkii/exblurf
공식 코드ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images
https://github.com/tangtaogo/alignmif
공식 코드AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
https://github.com/tangtaogo/lidar-nerf
공식 코드LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
https://github.com/tberriel/featsplat
공식 코드Feature Splatting for Better Novel View Synthesis with Low Overlap
https://github.com/tehret/diffnerf
공식 코드https://github.com/tfwang-9527/gs-3i
공식 코드GS-I$^{3}$: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent Images
https://github.com/thomasneff/AdaNeRF
공식 코드AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
https://github.com/thunguyenphuoc/HoloGAN
공식 코드HoloGAN: Unsupervised learning of 3D representations from natural images
https://github.com/tiantianwang/4d_video_stylization
공식 코드https://github.com/timwang2001/tc-gs
공식 코드TC-GS: Tri-plane based compression for 3D Gaussian Splatting
https://github.com/tinghuiz/appearance-flow
공식 코드Novel View Synthesis for Large-scale Scene using Adversarial Loss
https://github.com/toukaienn/ivr-gs
공식 코드iVR-GS: Inverse Volume Rendering for Explorable Visualization via Editable 3D Gaussian Splatting
https://github.com/trickygo/pano2room
공식 코드Pano2Room: Novel View Synthesis from a Single Indoor Panorama
https://github.com/trickygo/sinmpi
공식 코드SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images
https://github.com/tuallen/speede3dgs
공식 코드Speedy Deformable 3D Gaussian Splatting: Fast Rendering and Compression of Dynamic Scenes
https://github.com/tum-vision/intrinsic-neural-fields
공식 코드https://github.com/ucdvision/compact3d
공식 코드CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
https://github.com/ueda0319/neddf
공식 코드https://github.com/uzh-rpg/evdeblurnerf
공식 코드Mitigating Motion Blur in Neural Radiance Fields with Events and Frames
https://github.com/vastlik/symformer
공식 코드SymFormer: End-to-end symbolic regression using transformer-based architecture
https://github.com/vc-bonn/nerfs-are-mirror-detectors
공식 코드https://github.com/victor-rong/gstex
공식 코드https://github.com/vincentfung13/MINE
공식 코드MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
https://github.com/violetamenendez/svs-sparse-novel-view
공식 코드https://github.com/vision-sjtu/lightning-nerf
공식 코드Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving
https://github.com/vita-group/aug-nerf
공식 코드https://github.com/vita-group/gnt-move
공식 코드Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts
https://github.com/vlar-group/nvfi
공식 코드NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
https://github.com/vlar-group/osn
공식 코드OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
https://github.com/vppyw/Stylized-NeRF-with-AdaIN
공식 코드https://github.com/vt-vl-lab/3d-photo-inpainting
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.