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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 361370 of 517 papers

TitleStatusHype
Local Detection of Stereo Occlusion Boundaries0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks0
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios0
Multi-Level Context Ultra-Aggregation for Stereo Matching0
DISCO: Depth Inference from Stereo using Context0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Guided Stereo MatchingCode0
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
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