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
Latest papers with no code
Bayesian Learning for Disparity Map Refinement for Semi-Dense Active Stereo Vision
A major focus of recent developments in stereo vision has been on how to obtain accurate dense disparity maps in passive stereo vision.
Perception-Oriented Stereo Image Super-Resolution
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR.
ORA3D: Overlap Region Aware Multi-view 3D Object Detection
Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network.
Recovering Detail in 3D Shapes Using Disparity Maps
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images.
4D-MultispectralNet: Multispectral Stereoscopic Disparity Estimation using Human Masks
A lot of work has been done in classical stereoscopy, but multispectral stereoscopy is not studied as frequently.
Investigating Spherical Epipolar Rectification for Multi-View Stereo 3D Reconstruction
However, existing approaches face challenges in applying dense matching for images with different viewpoints primarily due to large differences in object scale.
OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation
Light field disparity estimation is an essential task in computer vision with various applications.
Disentangling Light Fields for Super-Resolution and Disparity Estimation
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation
Deep stereo matching has made significant progress in recent years.
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks
The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods.
