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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 4150 of 517 papers

TitleStatusHype
Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-worldCode1
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo MatchingCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
LiDAR-Event Stereo Fusion with HallucinationsCode1
Temporal Event Stereo via Joint Learning with Stereoscopic FlowCode1
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth ConversionCode1
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar ImageryCode1
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgeryCode1
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