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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 8190 of 517 papers

TitleStatusHype
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo MatchingCode1
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
Neural Rays for Occlusion-aware Image-based RenderingCode1
Bilateral Grid Learning for Stereo Matching NetworksCode1
ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite ImagesCode1
SRH-Net: Stacked Recurrent Hourglass Network for Stereo MatchingCode1
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