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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 7180 of 517 papers

TitleStatusHype
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
Advancing Applications of Satellite Photogrammetry: Novel Approaches for Built-up Area Modeling and Natural Environment Monitoring using Stereo/Multi-view Satellite Image-derived 3D Data0
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint0
Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective0
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