Stereo Matching Hand
35 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Stereo Matching Hand
Middlebury 2005
CATS
Middlebury 2006
Middlebury 2001
Most implemented papers
Pyramid Stereo Matching Network
JiaRenChang/PSMNet
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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
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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
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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
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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
The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.
Learning for Disparity Estimation through Feature Constancy
JiaRenChang/PSMNet
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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
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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
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
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
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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.
