Papers with Code 아카이브
Object Reconstruction
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jasonyzhang/relpose
RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
https://github.com/justanhduc/graphx-conv
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
https://github.com/kach/softraxterizer
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
https://github.com/kingcheng2000/3D-point-cloud-generation
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
https://github.com/kingcheng2000/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
https://github.com/kuangzijian/drifu-for-animals
https://github.com/laughtervv/tf_neural_renderer
https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/liuzhengzhe/dreamstone-iss
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/mattiagaggi/3D-Pose-Estimator---MSc_project
https://github.com/mees/self-supervised-3D
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
https://github.com/mikacuy/deformation_aware_embedding
https://github.com/natowi/3D-reconstruction-with-Neural-Networks
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/nywang16/Pixel2Mesh
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/oaeen/3D-point-cloud-generation
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
https://github.com/pranavbajoria93/3D_Reconstruction_3DR2N2
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/pravinthsam/Ilios-3D-model-generation
https://github.com/qweas120/PSVH-3d-reconstruction
Deep Single-View 3D Object Reconstruction with Visual Hull Embedding
https://github.com/raphaelsulzer/dsr-benchmark
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/raphaelsulzer/dsrv-data
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/richardsfc/pixel2mesh-ssds
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/shunsukesaito/PIFu
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
https://github.com/tensorflow/models/tree/master/research/ptn
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
https://github.com/trailab/generalobjectmapping
Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior
https://github.com/ttaa9/genren
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/ut-austin-rpl/forge
Few-View Object Reconstruction with Unknown Categories and Camera Poses
https://github.com/ventusff/neurecon
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
https://github.com/vnoves/aectech2019-sketchto3d-backend
Hierarchical Surface Prediction for 3D Object Reconstruction
https://github.com/wMito/HuSc3D
HuSc3D: Human Sculpture dataset for 3D object reconstruction
https://github.com/xhxuciedu/3DHand
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
https://github.com/xiaoqianruan1/partonomic_reconstruction
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
https://github.com/yongheng1991/3D-point-capsule-networks
https://github.com/yuzhenmao/MI_P2V
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
https://github.com/zc-alexfan/arctic
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
https://github.com/zxpzhong/3D-RecGAN-pytorch
3D Object Reconstruction from a Single Depth View with Adversarial Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.