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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 171180 of 517 papers

TitleStatusHype
Stereo Risk: A Continuous Modeling Approach to Stereo Matching0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation0
Rectified Iterative Disparity for Stereo Matching0
SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo MatchingCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks0
Distill-then-prune: An Efficient Compression Framework for Real-time Stereo Matching Network on Edge Devices0
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images0
Generalizable Novel-View Synthesis using a Stereo Camera0
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