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NeRF
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/snap-research/r2l
공식 코드R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
https://github.com/sntubix/denser
공식 코드DENSER: 3D Gaussians Splatting for Scene Reconstruction of Dynamic Urban Environments
https://github.com/srameo/le3d
공식 코드Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis
https://github.com/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/stelzner/obsurf
공식 코드Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation
https://github.com/stevliu/editnerf
공식 코드https://github.com/sunset1995/directvoxgo
공식 코드Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction
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/tangtaogo/lidar-nerf
공식 코드LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
https://github.com/tangzj/able-nerf
공식 코드ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field
https://github.com/tanqianq/enhance-nerf
공식 코드Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
https://github.com/tatakai1/evenerf
공식 코드Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields
https://github.com/tehret/diffnerf
공식 코드https://github.com/tensorflow/probability
공식 코드https://github.com/thanostriantafyllou3/ph-dropout
공식 코드PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis
https://github.com/threestudio-project/threestudio
공식 코드DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models
https://github.com/thucz/splatter360
공식 코드Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images
https://github.com/tianfr/mononerf
공식 코드MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos
https://github.com/tianfr/semantic-flow
공식 코드Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
https://github.com/tianxiangma/cnerf
공식 코드Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field
https://github.com/tom919654/C3G-NeRF
공식 코드Class-Continuous Conditional Generative Neural Radiance Field
https://github.com/tommy-nguyen-cpu/advirl
공식 코드AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models
https://github.com/toughstonex/styledyrf
공식 코드StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields
https://github.com/tqtqliu/gefu
공식 코드Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields
https://github.com/tuallen/speede3dgs
공식 코드Speedy Deformable 3D Gaussian Splatting: Fast Rendering and Compression of Dynamic Scenes
https://github.com/tyhuang0428/dreamcontrol
공식 코드DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
https://github.com/tyz1030/neuralsea
공식 코드https://github.com/ubc-vision/lsenerf
공식 코드LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
https://github.com/ucdvision/compact3d
공식 코드CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
https://github.com/ucwxb/graphavatar
공식 코드GraphAvatar: Compact Head Avatars with GNN-Generated 3D Gaussians
https://github.com/ueda0319/neddf
공식 코드https://github.com/uhhhci/immersive-ngp
공식 코드https://github.com/umarkhalidAI/LatentEditor
공식 코드https://github.com/uzh-rpg/evdeblurnerf
공식 코드Mitigating Motion Blur in Neural Radiance Fields with Events and Frames
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.