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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

Showing 51–60 of 517 papers

TitleStatusHype
Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo MethodsCode1
3D Surface Reconstruction From Multi-Date Satellite ImagesCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo SystemsCode1
AANet: Adaptive Aggregation Network for Efficient Stereo MatchingCode1
Context-Enhanced Stereo TransformerCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Global Occlusion-Aware Transformer for Robust Stereo MatchingCode1
FADNet: A Fast and Accurate Network for Disparity EstimationCode1
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