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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

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Showing 481–490 of 517 papers

TitleStatusHype
Leveraging Stereo Matching With Learning-Based Confidence Measures—0
LiDAR-guided Stereo Matching with a Spatial Consistency Constraint—0
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields—0
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation—0
LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery—0
Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene Recognition—0
Local Convolutional Features With Unsupervised Training for Image Retrieval—0
Local Detection of Stereo Occlusion Boundaries—0
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