https://bitbucket.org/visinf/projects-2018-matryoshka
공식 코드Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
Papers with Code 아카이브
3D geometry
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/63days/PartGlot
공식 코드PartGlot: Learning Shape Part Segmentation from Language Reference Games
https://github.com/ActiveVisionLab/MobileBrick
공식 코드MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices
https://github.com/ActiveVisionLab/NeFeS
공식 코드Neural Refinement for Absolute Pose Regression with Feature Synthesis
https://github.com/Angtian/NeMo
공식 코드NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
https://github.com/B1ueber2y/DIST-Renderer
공식 코드DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
https://github.com/CLinvx/HPM-MVS
공식 코드https://github.com/ForMyCat/SparseGS
공식 코드SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting
https://github.com/Gorilla-Lab-SCUT/tango
공식 코드TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
https://github.com/HenrryBryant/Scene-Recognition-in-3D
공식 코드https://github.com/JunzheJosephZhu/HiFA
공식 코드HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
https://github.com/KU-CVLAB/CoPoNeRF
공식 코드Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs
https://github.com/KU-CVLAB/DaRF
공식 코드DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation
https://github.com/KU-CVLAB/RetDream
공식 코드Retrieval-Augmented Score Distillation for Text-to-3D Generation
https://github.com/LMescheder/Occupancy-Networks
공식 코드Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/Lakonik/MVEdit
공식 코드3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation
https://github.com/NVlabs/eg3d
공식 코드https://github.com/PKUterran/HamNet
공식 코드HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
https://github.com/QuintonQu/gaussian-splatting-with-depth
공식 코드https://github.com/ShengyuH/Scene-Recognition-in-3D
공식 코드https://github.com/Shreeyak/cleargrasp
공식 코드ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
https://github.com/TongZhangTHU/sgr
공식 코드A Universal Semantic-Geometric Representation for Robotic Manipulation
https://github.com/Tsinghua-MARS-Lab/Occ3D
공식 코드Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
https://github.com/VCIP-RGBD/DFormer
공식 코드DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
https://github.com/WangStephen/DeepCA
공식 코드https://github.com/WangStephen/NeCA
공식 코드https://github.com/Xuanmeng-Zhang/MVCGAN
공식 코드Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis
https://github.com/XuelianCheng/LidarStereoNet
공식 코드https://github.com/Yang7879/3D-RecGAN
공식 코드3D Object Reconstruction from a Single Depth View with Adversarial Learning
https://github.com/Yang7879/3D-RecGAN-extended
공식 코드https://github.com/ZqlwMatt/Tex4D
공식 코드Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models
https://github.com/abdallahdib/NextFace
공식 코드Practical Face Reconstruction via Differentiable Ray Tracing
https://github.com/aiart-hdu/hida
공식 코드Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation
https://github.com/ais-bonn/jetsontrtperception
공식 코드3D Semantic Scene Perception using Distributed Smart Edge Sensors
https://github.com/ais-bonn/smartedgesensor3dsceneperception
공식 코드3D Semantic Scene Perception using Distributed Smart Edge Sensors
https://github.com/alexfrom0815/ir-bpp
공식 코드Learning Physically Realizable Skills for Online Packing of General 3D Shapes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.