Stereo Matching
150 papers with code • 0 benchmarks • 18 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
Benchmarks
These leaderboards are used to track progress in Stereo Matching
Libraries
Use these libraries to find Stereo Matching models and implementationsDatasets
Virtual KITTI
ETH3D
Middlebury 2014
DrivingStereo
Virtual KITTI 2
PST900
IMC PhotoTourism
3D Ken Burns
Middlebury 2005
CATS
Most implemented papers
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
google-research/google-research
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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
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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
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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
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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
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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
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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.
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
jytime/DICL-Flow
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NeurIPS 2020
Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volume to learn a 3D cost volume.
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching
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
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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
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.
