https://github.com/azuxmioy/Render-In-Between
공식 코드Render In-between: Motion Guided Video Synthesis for Action Interpolation
Papers with Code 아카이브
Neural Rendering
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Given a representation of a 3D scene of some kind (point cloud, mesh, voxels, etc.), the task is to create an algorithm that can produce photorealistic renderings of this scene from an arbitrary viewpoint. Sometimes, the task is accompanied by image/scene appearance manipulation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/azzarelli/wape
공식 코드https://github.com/benhenryl/sharpnerf
공식 코드Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior
https://github.com/bigcileng/bilateral-driving
공식 코드Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
https://github.com/bmild/nerf
공식 코드NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/boese0601/rc-mvsnet
공식 코드RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
https://github.com/carlinds/splatad
공식 코드SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
https://github.com/charlesyou999648/morse
공식 코드Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
https://github.com/chensjtu/gaussianobject
공식 코드GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting
https://github.com/city-super/Octree-GS
공식 코드Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians
https://github.com/city-super/Scaffold-GS
공식 코드Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
https://github.com/cmlab-korea/awesome-3d-low-level-vision
공식 코드R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
https://github.com/computational-imaging/automatic-integration
공식 코드AutoInt: Automatic Integration for Fast Neural Volume Rendering
https://github.com/crishy1995/headnerf
공식 코드https://github.com/cvg/labelmaker
공식 코드LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
https://github.com/daniel03c1/masked_wavelet_nerf
공식 코드Masked Wavelet Representation for Compact Neural Radiance Fields
https://github.com/darglein/ADOP
공식 코드https://github.com/darglein/NeAT
공식 코드https://github.com/darthgera123/Appearance-Editing
공식 코드https://github.com/deepmind/spriteworld
공식 코드Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
https://github.com/deepshwang/vegs
공식 코드VEGS: View Extrapolation of Urban Scenes in 3D Gaussian Splatting using Learned Priors
https://github.com/dengnianchen/fovnerf
공식 코드FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality
https://github.com/ephemeral182/uwnr
공식 코드Underwater Light Field Retention : Neural Rendering for Underwater Imaging
https://github.com/epic-kitchens/epic-fields-code
공식 코드https://github.com/ethz-asl/ucsa_neural_rendering
공식 코드Unsupervised Continual Semantic Adaptation through Neural Rendering
https://github.com/fabiotosi92/nerf-supervised-deep-stereo
공식 코드https://github.com/facebookresearch/AnimalAvatar
공식 코드Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
https://github.com/facebookresearch/NeuralPassthrough
공식 코드https://github.com/facebookresearch/co3d
공식 코드Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
https://github.com/fdarmon/neuralwarp
공식 코드Improving neural implicit surfaces geometry with patch warping
https://github.com/foivospar/NED
공식 코드https://github.com/fraunhofer-iis/livenvs
공식 코드https://github.com/fraunhoferhhi/neural-deferred-shading
공식 코드https://github.com/gap-lab-cuhk-sz/rec-mv
공식 코드REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
https://github.com/georghess/neurad-studio
공식 코드https://github.com/girish-03/deepsemmanipulation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.