# Dense Pixel Correspondence Estimation

14 papers with code • 4 benchmarks • 3 datasets

## Libraries

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

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

Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.

# Optical Flow Estimation using a Spatial Pyramid Network

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

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

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

We propose GOCor, a fully differentiable dense matching module, acting as a direct replacement to the feature correlation layer.

# DeepMatching: Hierarchical Deformable Dense Matching

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

# Space-Time Correspondence as a Contrastive Random Walk

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.