Stereo Matching Hand

35 papers with code • 0 benchmarks • 5 datasets

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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.

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.

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.

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.

Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

DeepMotionAIResearch/DenseMatchingBenchmark 9 Sep 2019

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

brainless-afk/Binary_Stereo_Matching 10 Feb 2014

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

rookiepig/CrossScaleStereo CVPR 2014

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.