Disparity Estimation
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Greatest papers with code
Learning for Disparity Estimation through Feature Constancy
CVPR 2018
• JiaRenChang/PSMNet
• 
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
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.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
ICCV 2019
• uber-research/DeepPruner
•
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.
AUTONOMOUS DRIVING DISPARITY ESTIMATION IMAGE RECONSTRUCTION STEREO MATCHING
Understanding and Robustifying Differentiable Architecture Search
ICLR 2020
• automl/RobustDARTS
•
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.
DISPARITY ESTIMATION IMAGE CLASSIFICATION LANGUAGE MODELLING
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.
FADNet: A Fast and Accurate Network for Disparity Estimation
• HKBU-HPML/FADNet
•
Deep neural networks (DNNs) have achieved great success in the area of computer vision.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
CVPR 2016
• HKBU-HPML/FADNet
•
By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
DISPARITY ESTIMATION OPTICAL FLOW ESTIMATION SCENE FLOW ESTIMATION
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
• zju3dv/disprcnn
•
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.
Fast Disparity Estimation using Dense Networks
Disparity estimation is a difficult problem in stereo vision because the correspondence technique fails in images with textureless and repetitive regions.
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.