Disparity Estimation
47 papers with code • 4 benchmarks • 4 datasets
The Disparity Estimation is the task of finding the pixels in the multiscopic views that correspond to the same 3D point in the scene.
Benchmarks
These leaderboards are used to track progress in Disparity Estimation
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FlyingThings3D
Middlebury 2014
IRS
Dynamic Replica
Most implemented papers
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
HKBU-HPML/FADNet
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CVPR 2016
By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
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.
Learning for Disparity Estimation through Feature Constancy
JiaRenChang/PSMNet
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CVPR 2018
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
DeepMotionAIResearch/DenseMatchingBenchmark
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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.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
gallenszl/pcwnet
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First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
SMD-Nets: Stereo Mixture Density Networks
fabiotosi92/SMD-Nets
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CVPR 2021
Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging.
Blur aware metric depth estimation with multi-focus plenoptic cameras
A method to calibrate the inverse model is then proposed.
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
Instead, we propose a generic architecture that decomposes the label improvement task to three steps: 1) detecting the initial label estimates that are incorrect, 2) replacing the incorrect labels with new ones, and finally 3) refining the renewed labels by predicting residual corrections w. r. t.
