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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 61–70 of 517 papers

TitleStatusHype
DCVSMNet: Double Cost Volume Stereo Matching NetworkCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Deep 3D Portrait from a Single ImageCode1
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo MatchingCode1
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view StereoCode1
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
Deep Laparoscopic Stereo Matching with TransformersCode1
Group-wise Correlation Stereo NetworkCode1
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented FeatureCode1
Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching TransformerCode1
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