https://github.com/PruneTruong/DenseMatching
공식 코드Learning Accurate Dense Correspondences and When to Trust Them
Papers with Code 아카이브
Geometric Matching
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PruneTruong/PDCNet
공식 코드Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/SunghwanHong/Deep-Matching-Prior
공식 코드Deep Matching Prior: Test-Time Optimization for Dense Correspondence
https://github.com/TRI-ML/KP3D
공식 코드Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
https://github.com/YuhangMing/Object-Guided-Relocalisation
공식 코드Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation
https://github.com/axelbarroso/scalenet
공식 코드https://github.com/benquick123/c-vton
공식 코드https://github.com/cguangyan-bit/dit
공식 코드Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction
https://github.com/dqiaole/matchflow
공식 코드Rethinking Optical Flow from Geometric Matching Consistent Perspective
https://github.com/juhongm999/chm
공식 코드https://github.com/lzx551402/contextdesc
공식 코드ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
https://github.com/parskatt/dkm
공식 코드DKM: Dense Kernelized Feature Matching for Geometry Estimation
https://github.com/rocketappslab/SCA-GAN
공식 코드https://github.com/sergeywong/cp-vton
공식 코드Toward Characteristic-Preserving Image-based Virtual Try-On Network
https://github.com/shngjz/pmatch
공식 코드PMatch: Paired Masked Image Modeling for Dense Geometric Matching
https://github.com/sungonce/cvnet
공식 코드https://github.com/wuuu3511/lamvsnet
공식 코드https://github.com/yutinghe-list/gvsl
공식 코드Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training
https://github.com/JaZz-9/Try-First
Toward Characteristic-Preserving Image-based Virtual Try-On Network
https://github.com/MosbehBarhoumi/VITON-PRE-PROCESSING
https://github.com/Octavio-Pappalardo/unsupervised-pretraining-via-self-classification
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
https://github.com/PruneTruong/GLU-Net
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
https://github.com/PruneTruong/GOCor
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
https://github.com/Semanti1/cnngeometric_pytorch
Convolutional neural network architecture for geometric matching
https://github.com/Wuschelbueb/AML19-SelfSupervised
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
https://github.com/ankitajaiswal00/Try-First
Toward Characteristic-Preserving Image-based Virtual Try-On Network
https://github.com/arunt-sjsu/DL_Project
Toward Characteristic-Preserving Image-based Virtual Try-On Network
https://github.com/edwardguil/CVNet
https://github.com/google-research/sparf
Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/ignacio-rocco/cnngeometric_pytorch
Convolutional neural network architecture for geometric matching
https://github.com/jaehyunnn/cnngeometric_tensorflow
Convolutional neural network architecture for geometric matching
https://github.com/jiayunz/Virtual-Try-On
Toward Characteristic-Preserving Image-based Virtual Try-On Network
https://github.com/levindabhi/SieveNet
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
https://github.com/myeongmy/aliasing_removal
Convolutional neural network architecture for geometric matching
https://github.com/qliu24/render-3d-segmentation
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
https://github.com/yoyongbo/test
Convolutional neural network architecture for geometric matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.