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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 381390 of 517 papers

TitleStatusHype
Real-Time Dense Stereo Embedded in A UAV for Road Inspection0
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow EstimationCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Noise-Aware Unsupervised Deep Lidar-Stereo FusionCode0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks0
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume NormalizationCode0
Group-wise Correlation Stereo NetworkCode1
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event camerasCode0
EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection0
Unsupervised Cross-spectral Stereo Matching by Learning to SynthesizeCode0
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