Stereo Matching Hand
35 papers with code • 0 benchmarks • 6 datasets
These leaderboards are used to track progress in Stereo Matching Hand
Most implemented papers
Pyramid Stereo Matching Network
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
In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation.
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
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
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
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
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
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.