https://github.com/hustvl/symphonies
공식 코드Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
Papers with Code 아카이브
Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hyperparams-free/hyperparams-free-3D-losses
공식 코드Hyperparameter-Free Losses for Model-Based Monocular Reconstruction
https://github.com/inception3d/easi3r
공식 코드Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
https://github.com/ispc-lab/lidar4d
공식 코드LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
https://github.com/j-fhu/udcunet
공식 코드UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network
https://github.com/jby1993/selfreconcode
공식 코드SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video
https://github.com/jfzhang95/avatargen
공식 코드AvatarGen: a 3D Generative Model for Animatable Human Avatars
https://github.com/jiahuilei/mosca
공식 코드MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
https://github.com/jieyang1987/Tone-Mapping-Based-on-Multi-scale-Histogram-Synthesis
공식 코드https://github.com/jqtangust/epce-hdr
공식 코드High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation
https://github.com/jzr99/Geo4D
공식 코드Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
https://github.com/kerrj/rsrd
공식 코드Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction
https://github.com/kirstenmaas/nerf-ca
공식 코드NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
https://github.com/kuangzijian/drifu-for-animals
공식 코드https://github.com/kwaivgi/syncammaster
공식 코드SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints
https://github.com/laughtervv/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/layumi/3D-Magic-Mirror
공식 코드3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
https://github.com/linmc86/Deep-Reformulated-Laplacian-Tone-Mapping
공식 코드https://github.com/liuyuan-pal/syncdreamer
공식 코드SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
https://github.com/loping151/lerplane
공식 코드Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues
https://github.com/lwt3437/lanet
공식 코드Luminance Attentive Networks for HDR Image and Panorama Reconstruction
https://github.com/marcopesavento/Super-resolution-3D-Human-Shape-from-a-Single-Low-Resolution-Image
공식 코드Super-resolution 3D Human Shape from a Single Low-Resolution Image
https://github.com/markomih/ResFields
공식 코드ResFields: Residual Neural Fields for Spatiotemporal Signals
https://github.com/mdyao/csnorm
공식 코드Generalized Lightness Adaptation with Channel Selective Normalization
https://github.com/mks0601/Hand4Whole_RELEASE
공식 코드Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation
https://github.com/mks0601/I2L-MeshNet_RELEASE
공식 코드https://github.com/monniert/unicorn
공식 코드Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
https://github.com/muelea/buddi
공식 코드Generative Proxemics: A Prior for 3D Social Interaction from Images
https://github.com/muelea/shapy
공식 코드Accurate 3D Body Shape Regression using Metric and Semantic Attributes
https://github.com/mukulkhanna/fhdr
공식 코드FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network
https://github.com/mv-lab/AISP
공식 코드Simple Image Signal Processing using Global Context Guidance
https://github.com/mv-lab/nilut
공식 코드NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
https://github.com/naver/multi-hmr
공식 코드Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
https://github.com/nicolaihaeni/corn
공식 코드Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
https://github.com/nicolas-dufour/SCAM
공식 코드SCAM! Transferring humans between images with Semantic Cross Attention Modulation
https://github.com/nitinagarwal/GAMesh
공식 코드GAMesh: Guided and Augmented Meshing for Deep Point Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.