https://github.com/iddifficult/ni_nerf
공식 코드NeRF-based CBCT Reconstruction needs Normalization and Initialization
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NeRF-based CBCT Reconstruction needs Normalization and Initialization
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis
SG-NeRF: Neural Surface Reconstruction with Scene Graph Optimization
Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting
Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives
SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields
FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models
FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra
ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers
NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects
Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields
SIn-NeRF2NeRF: Editing 3D Scenes with Instructions through Segmentation and Inpainting
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.