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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
FlyingThings3D
Middlebury 2014
IRS
Dynamic Replica
Latest papers with no code
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction
Recent works have shown that depth information can be obtained from Dual-Pixel (DP) sensors.
Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty.
Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction
Depth estimation from light field (LF) images is a fundamental step for numerous applications.
Regularizing disparity estimation via multi task learning with structured light reconstruction
Secondly, we \textcolor{black}{explore the use of a multi task learning (MTL) framework for the joint training of structured light and disparity.
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity
In this paper, we present a novel spatially-adaptive self-similarity (SASS) for unsupervised asymmetric stereo matching.
Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image
In this work, we propose a self-supervised learning method that learns bidirectional disparity by utilizing the nature of anisotropic blur kernels in dual-pixel photography.
Disparity estimation for fisheye images with an application to intermediate view synthesis
A straightforward approach to disparity estimation is block matching, which performs well with perspective data.
Stereo Image Rain Removal via Dual-View Mutual Attention
Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e. g., rain removal and super-resolution.
Matching entropy based disparity estimation from light field
A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions.
Fast Disparity Estimation from a Single Compressed Light Field Measurement
Specifically, we propose to jointly optimize an optical architecture for acquiring a single coded light field snapshot and a convolutional neural network (CNN) for estimating the disparity maps.
