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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 351360 of 517 papers

TitleStatusHype
Learning Dense Wide Baseline Stereo Matching for People0
Real-Time Semantic Stereo Matching0
Semantic Stereo Matching With Pyramid Cost Volumes0
Object-Centric Stereo Matching for 3D Object Detection0
Real-Time Variational Fisheye Stereo without Rectification and UndistortionCode0
Learning Residual Flow as Dynamic Motion from Stereo Videos0
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchCode0
Adaptive Unimodal Cost Volume Filtering for Deep Stereo MatchingCode0
Robust Full-FoV Depth Estimation in Tele-wide Camera System0
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task0
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