https://github.com/jmorlana/dran
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
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/junyuandeng/dm-calib
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
https://github.com/kairenye/LidarStereoFusion
https://github.com/kieran514/dyna-dm
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/koryako/selfcarRaspberryPi
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
https://github.com/kyleolsz/hairgen
Intuitive, Interactive Beard and Hair Synthesis with Generative Models
https://github.com/lkny123/agn
Conditional Antibody Design as 3D Equivariant Graph Translation
https://github.com/marian42/mesh_to_sdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/marian42/shapegan
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/matanatz/SAL
https://github.com/mattiagaggi/3D-Pose-Estimator---MSc_project
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
https://github.com/maurock/deepsdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/mindspore-ai/models/tree/master/official/cv/depthnet
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/ml4bio/rna-fm
https://github.com/mohamedelrefaie/drivaernet
TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
https://github.com/monkey0head/Domain_Adaptation_thesis
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
https://github.com/nkolot/GraphCMR
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/nv-tlabs/DIB-R
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
https://github.com/nywang16/Pixel2Mesh
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/optas/shapeglot
PartGlot: Learning Shape Part Segmentation from Language Reference Games
https://github.com/paulpanwang/POPE
POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference
https://github.com/pedrobvidal/insightface
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
https://github.com/pravinthsam/Ilios-3D-model-generation
https://github.com/qizekun/ReCon
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/raphaelsulzer/dsr-benchmark
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/raphaelsulzer/dsrv-data
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/raunaks13/carla-SIGNet
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
https://github.com/rehg-lab/3dshapegen
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/richardsfc/pixel2mesh-ssds
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/rish-16/rna-backbone-design
https://github.com/runpeidong/act
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/sejong-rcv/2021.Paper.TransDSSL
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/sergeyprokudin/smplpix
https://github.com/shaharzuler/cardio_volume_skewer
https://github.com/shuuchen/depth_eigen
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
https://github.com/snldmt/bags
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.