Geometric Matching
24 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Geometric Matching
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Libraries
Use these libraries to find Geometric Matching models and implementations
HPatches
Most implemented papers
Convolutional neural network architecture for geometric matching
ignacio-rocco/cnngeometric_pytorch
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CVPR 2017
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.
Toward Characteristic-Preserving Image-based Virtual Try-On Network
sergeywong/cp-vton
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ECCV 2018
Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.
Learning Accurate Dense Correspondences and When to Trust Them
PruneTruong/PDCNet
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CVPR 2021
Establishing dense correspondences between a pair of images is an important and general problem.
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
PruneTruong/GLU-Net
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CVPR 2020
Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences.
GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
PruneTruong/GLU-Net
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NeurIPS 2020
We propose GOCor, a fully differentiable dense matching module, acting as a direct replacement to the feature correlation layer.
Correlation Verification for Image Retrieval
Geometric verification is considered a de facto solution for the re-ranking task in image retrieval.
C-VTON: Context-Driven Image-Based Virtual Try-On Network
At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result.
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Wuschelbueb/AML19-SelfSupervised
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While such generic features cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
lzx551402/contextdesc
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CVPR 2019
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
TRI-ML/KP3D
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Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion.
