Dense Pixel Correspondence Estimation

14 papers with code • 4 benchmarks • 3 datasets

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Use these libraries to find Dense Pixel Correspondence Estimation models and implementations

Most implemented papers

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

NVlabs/PWC-Net CVPR 2018

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 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 CVPR 2017

We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning.

DGC-Net: Dense Geometric Correspondence Network

AaltoVision/DGC-Net 19 Oct 2018

This paper addresses the challenge of dense pixel correspondence estimation between two images.

Learning Accurate Dense Correspondences and When to Trust Them

PruneTruong/DenseMatching 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 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 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 25 Jun 2015

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images.

Space-Time Correspondence as a Contrastive Random Walk

ajabri/videowalk NeurIPS 2020

We cast correspondence as prediction of links in a space-time graph constructed from video.

COTR: Correspondence Transformer for Matching Across Images

ubc-vision/COTR ICCV 2021

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.