Stereo Matching Hand

35 papers with code • 0 benchmarks • 6 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Pyramid Stereo Matching Network

JiaRenChang/PSMNet CVPR 2018

The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume.

Efficient Deep Learning for Stereo Matching

saakuraa/cvpr16_stereo_public CVPR 2016

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation.

Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

XuelianCheng/LidarStereoNet CVPR 2019

In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps.

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

jzbontar/mc-cnn 20 Oct 2015

We approach the problem by learning a similarity measure on small image patches using a convolutional neural network.

Continuous 3D Label Stereo Matching using Local Expansion Moves

t-taniai/LocalExpStereo 28 Mar 2016

The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.

Learning for Disparity Estimation through Feature Constancy

JiaRenChang/PSMNet CVPR 2018

The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.

StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

meteorshowers/StereoNet ECCV 2018

A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.

Hierarchical Discrete Distribution Decomposition for Match Density Estimation

ucbdrive/hd3 CVPR 2019

Explicit representations of the global match distributions of pixel-wise correspondences between pairs of images are desirable for uncertainty estimation and downstream applications.

Group-wise Correlation Stereo Network

xy-guo/GwcNet CVPR 2019

Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps.

OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching

hyu-cvlab/omnimvs-pytorch ICCV 2019

The 3D encoder-decoder block takes the aligned feature volume to produce the omnidirectional depth estimate with regularization on uncertain regions utilizing the global context information.