Dense Pixel Correspondence Estimation
14 papers with code • 4 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Dense Pixel Correspondence Estimation
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Libraries
Use these libraries to find Dense Pixel Correspondence Estimation models and implementations
KITTI
HPatches
ETH3D
Most implemented papers
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
NVIDIA/flownet2-pytorch
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CVPR 2017
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
Optical Flow Estimation using a Spatial Pyramid Network
anuragranj/spynet
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CVPR 2017
We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning.
DGC-Net: Dense Geometric Correspondence Network
This paper addresses the challenge of dense pixel correspondence estimation between two images.
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.
DeepMatching: Hierarchical Deformable Dense Matching
vwegn/dm
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We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images.
Space-Time Correspondence as a Contrastive Random Walk
ajabri/videowalk
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NeurIPS 2020
We cast correspondence as prediction of links in a space-time graph constructed from video.
COTR: Correspondence Transformer for Matching Across Images
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
