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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 291–300 of 517 papers

TitleStatusHype
Stereo Matching by Self-supervision of Multiscopic Vision—0
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup—0
Physics-based Differentiable Depth Sensor Simulation—0
Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching—0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation—0
Stereo Object Matching Network—0
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images—0
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching—0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
An underwater binocular stereo matching algorithm based on the best search domain—0
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