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/google-research/google-research/tree/master/dreamfields
공식 코드https://github.com/google-research/google-research/tree/master/light_field_neural_rendering
공식 코드https://github.com/haeyun-choi/DeepDeblurRF
공식 코드https://github.com/haidongz-usc/CAT-NeRF
공식 코드CAT-NeRF: Constancy-Aware Tx$^2$Former for Dynamic Body Modeling
https://github.com/haidongz-usc/CaesarNeRF
공식 코드CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
https://github.com/haksorus/gsplatloc
공식 코드https://github.com/haoyizhu/realrobot
공식 코드SPA: 3D Spatial-Awareness Enables Effective Embodied Representation
https://github.com/henry123-boy/Level-S2FM_official
공식 코드Level-S$^2$fM: Structure from Motion on Neural Level Set of Implicit Surfaces
https://github.com/howiema/cvthead
공식 코드CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer
https://github.com/hse1032/Adversarial-Generation-of-Hierarchical-Gaussians-for-3D-Generative-Model
공식 코드GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats
https://github.com/huajianup/photo-slam
공식 코드https://github.com/humansensinglab/Generalizable-Human-Gaussians
공식 코드https://github.com/ingra14m/deformable-3d-gaussians
공식 코드Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
https://github.com/initialneil/DEGAS
공식 코드https://github.com/jasonyzhang/ners
공식 코드NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
https://github.com/jby1993/selfreconcode
공식 코드SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video
https://github.com/jenningsl/nerf-loc
공식 코드NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
https://github.com/jianglongye/featurenerf
공식 코드FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models
https://github.com/jiawei-yang/freenerf
공식 코드FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
https://github.com/jiaxiongq/neus-hsr
공식 코드Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections
https://github.com/jingyangcarl/LightSamplingField
공식 코드Light Sampling Field and BRDF Representation for Physically-based Neural Rendering
https://github.com/jkulhanek/viewformer
공식 코드ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers
https://github.com/josh-tobin/egqn-datasets
공식 코드https://github.com/juewenpeng/bokehme
공식 코드https://github.com/ken-ouyang/PGMPI
공식 코드Real-Time Neural Character Rendering with Pose-Guided Multiplane Images
https://github.com/klmav-cuc/golf-nrt
공식 코드GoLF-NRT: Integrating Global Context and Local Geometry for Few-Shot View Synthesis
https://github.com/lfranke/vet
공식 코드VET: Visual Error Tomography for Point Cloud Completion and High-Quality Neural Rendering
https://github.com/likojack/bnv_fusion
공식 코드BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
https://github.com/linhanwang/DC-Gaussian
공식 코드DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos
https://github.com/linshan-bin/occnerf
공식 코드OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments
https://github.com/liuyuan-pal/NeuRay
공식 코드https://github.com/liuyuan-pal/nero
공식 코드NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images
https://github.com/magdalena-wysocki/ultra-nerf
공식 코드https://github.com/marcoamonteiro/pi-GAN
공식 코드pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
https://github.com/markomih/ResFields
공식 코드ResFields: Residual Neural Fields for Spatiotemporal Signals
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