Papers with Code 아카이브
NeRF
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/georghess/neurad
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
https://github.com/georghess/neurad-studio
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
https://github.com/glouppe/info8010-deep-learning
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/google/hypernerf
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
https://github.com/google/samurai
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
https://github.com/happytianhao/tade
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
https://github.com/hjr37/CG-SLAM
CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field
https://github.com/hjxwhy/mipnerf_pl
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
https://github.com/hku-mars/gs-sdf
https://github.com/hkust-vgd/nerfstyle
https://github.com/huangkun101230/360mtlgeometricestimation
GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces
https://github.com/husheng12345/Unofficial-NeRF-Supervised-Deep-Stereo
https://github.com/hustvl/4DGaussians
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
https://github.com/hustvl/NeuSample
https://github.com/idiap/eslam
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
https://github.com/idiap/geonerf
https://github.com/ishanchadha01/lightneus
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
https://github.com/jamesperlman/nerfrendercore
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/jasonyzhang/relpose
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
https://github.com/jchibane/srf
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
https://github.com/jingma-git/NeRF_Pytorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/kakaobrain/NeRF-Factory
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/kangpeilun/VastGaussian
VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction
https://github.com/ken2576/vision-nerf
Vision Transformer for NeRF-Based View Synthesis from a Single Input Image
https://github.com/keras-team/keras-io/blob/master/examples/vision/nerf.py
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/kklemon/gon-pytorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/komeiharada/learn_DL_together
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/krrish94/nerf-pytorch
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/kv2000/DeepCharacters_Pytorch
https://github.com/kwea123/nerf_pl
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/laura-a-n-n/tf-garf
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
https://github.com/lightwheelai/street-gaussians-ns
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
https://github.com/loping151/lerplane
Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane
https://github.com/matsuren/nerf_jax_flax_practice
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
https://github.com/memoatwit/universalperturbation
AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.