Disparity Estimation
37 papers with code • 4 benchmarks • 3 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
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.
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.
Superpixel Segmentation with Fully Convolutional Networks
fuy34/superpixel_fcn
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CVPR 2020
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.
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.
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.
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions.
Variational Disparity Estimation Framework for Plenoptic Image
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images.
