Stereo Matching

83 papers with code • 0 benchmarks • 13 datasets

Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Greatest papers with code

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

google-research/google-research CVPR 2021

Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.

Stereo Depth Estimation Stereo Disparity Estimation +1

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.

Stereo Depth Estimation Stereo-LiDAR Fusion +2

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.

Disparity Estimation Stereo Matching +1

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.

Stereo Matching Stereo Matching Hand

GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

feihuzhang/GANet CVPR 2019

In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities.

Stereo Matching

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.

Quantization Stereo Depth Estimation +2

SOS: Stereo Matching in O(1) with Slanted Support Windows

meteorshowers/X-StereoLab 1 Jan 2018

Our key insight is that local smoothness can in fact be used to amortize the computation not only within initialization, but across the entire stereo pipeline.

Stereo Depth Estimation Stereo Disparity Estimation +1

Learning Depth with Convolutional Spatial Propagation Network

XinJCheng/CSPN 4 Oct 2018

In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks.

Depth Completion Depth Estimation +2

AANet: Adaptive Aggregation Network for Efficient Stereo Matching

haofeixu/aanet CVPR 2020

Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved.

Stereo Disparity Estimation Stereo Matching