Stereo Depth Estimation
53 papers with code • 5 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Stereo Depth Estimation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Stereo Depth Estimation models and implementationsDatasets
KITTI
Spring
Helvipad
VBR
GUISS dataset
Most implemented papers
Efficient Attention: Attention with Linear Complexities
cmsflash/efficient-attention
•
•
Dot-product attention has wide applications in computer vision and natural language processing.
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
google-research/google-research
•
•
CVPR 2021
Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.
Pyramid Stereo Matching Network
JiaRenChang/PSMNet
•
•
CVPR 2018
The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume.
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Anytime Stereo Image Depth Estimation on Mobile Devices
mileyan/AnyNet
•
•
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
feihuzhang/GANet
•
•
CVPR 2019
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities.
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
cogsys-tuebingen/mobilestereonet
•
•
Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
meteorshowers/StereoNet
•
•
ECCV 2018
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions
aasharma90/NighttimeDepthandFlow
•
•
To address the problem, we introduce a network joining day/night translation and stereo.
Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
