Stereo Matching

133 papers with code • 0 benchmarks • 17 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


Use these libraries to find Stereo Matching models and implementations

Most implemented papers

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.

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.

Efficient Deep Learning for Stereo Matching

saakuraa/cvpr16_stereo_public CVPR 2016

In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation.

Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

alibaba/cascade-stereo CVPR 2020

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.

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.

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.

CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

gallenszl/CFNet CVPR 2021

In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network.

MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching

cogsys-tuebingen/mobilestereonet 22 Aug 2021

Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively.

Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

megvii-research/crestereo CVPR 2022

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

gangweix/acvnet 23 Sep 2022

In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume.