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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 6170 of 517 papers

TitleStatusHype
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching0
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework0
Temporally Consistent Stereo MatchingCode2
Temporal Event Stereo via Joint Learning with Stereoscopic FlowCode1
A Survey on Deep Stereo Matching in the TwentiesCode4
Category-level Object Detection, Pose Estimation and Reconstruction from Stereo Images0
Stereo Risk: A Continuous Modeling Approach to Stereo Matching0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost AggregationCode0
Rectified Iterative Disparity for Stereo Matching0
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