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
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
Digging Into Normal Incorporated Stereo Matching
Magicboomliu/NINet
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ACM International Conference on Multimedia 2022
To enhance geometric consistency, especially in low-texture regions, the estimated normal map is then leveraged to calculate a local affinity matrix, providing the residual learning with information about where the correction should refer and thus improving the residual learning efficiency.
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images.
X-maps: Direct Depth Lookup for Event-based Structured Light Systems
We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras.
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
cveo/s3net
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Stereo matching and semantic segmentation are significant tasks in binocular satellite 3D reconstruction.
Global Occlusion-Aware Transformer for Robust Stereo Matching
Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bottleneck.
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System
Different from most former disparity estimation methods that operate in a frame-wise manner, our network acquires disparity maps in a temporally incremental way.
Blur aware metric depth estimation with multi-focus plenoptic cameras
A method to calibrate the inverse model is then proposed.
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
To address this issue and achieve a better trade-off between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation.
VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette
To address the aforementioned challenges, this paper proposes a novel approach where a high-resolution convolutional neural network is used to better capture relationships between the two spectra.
