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Stereo Matching

Stereo Matching is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Papers

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Showing 321–330 of 517 papers

TitleStatusHype
LSM: Learning Subspace Minimization for Low-level Vision—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching—0
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching—0
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo MatchingCode1
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Superpixel Segmentation with Fully Convolutional NetworksCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
Scene Completeness-Aware Lidar Depth Completion for Driving ScenarioCode1
Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning—0
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