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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 431–440 of 517 papers

TitleStatusHype
Robust Pseudo Random Fields for Light-Field Stereo Matching—0
Robust Visual SLAM with Point and Line Features—0
Rotational Crossed-Slit Light Field—0
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving—0
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction—0
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