# Stereo Matching Hand

35 papers with code • 0 benchmarks • 5 datasets

## Benchmarks

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.

# 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.

# 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.

# 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.

# Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.

# Binary Stereo Matching

In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem.

# Cross-Scale Cost Aggregation for Stereo Matching

We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels.