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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 401–410 of 517 papers

TitleStatusHype
Depth Refinement for Improved Stereo Reconstruction—0
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching—0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
DiffuVolume: Diffusion Model for Volume based Stereo Matching—0
Direct Depth Learning Network for Stereo Matching—0
Direction Matters: Depth Estimation With a Surface Normal Classifier—0
Direct Monocular Odometry Using Points and Lines—0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
Disjoint Pose and Shape for 3D Face Reconstruction—0
Displacement-Invariant Cost Computation for Efficient Stereo Matching—0
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