Stereo Depth Estimation

48 papers with code • 5 benchmarks • 5 datasets

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Use these libraries to find Stereo Depth Estimation models and implementations

Most implemented papers

Efficient Attention: Attention with Linear Complexities

cmsflash/efficient-attention 4 Dec 2018

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

NVIDIA-Jetson/redtail 26 Mar 2018

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 26 Oct 2018

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 22 Aug 2021

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 30 Sep 2019

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

puzzlepaint/camera_calibration 5 Dec 2019

In contrast, generic camera models allow for very accurate calibration due to their flexibility.