Disparity Estimation
52 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
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.
Learning on the Edge: Explicit Boundary Handling in CNNs
stfc-sciml/differentialconv2d
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Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding.
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.
Road surface 3d reconstruction based on dense subpixel disparity map estimation
To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used.
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
jspenmar/SAND_features
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CVPR 2019
In all cases, we show how incorporating SAND features results in better or comparable results to the baseline, whilst requiring little to no additional training.
Multi-View Stereo by Temporal Nonparametric Fusion
The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing information from previous views.
AutoDispNet: Improving Disparity Estimation With AutoML
lmb-freiburg/autodispnet
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ICCV 2019
In this work, we show how to use and extend existing AutoML techniques to efficiently optimize large-scale U-Net-like encoder-decoder architectures.
Understanding and Robustifying Differentiable Architecture Search
automl/RobustDARTS
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ICLR 2020
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.
