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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 421430 of 517 papers

TitleStatusHype
Fast Feature Extraction with CNNs with Pooling LayersCode0
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Left-Right Comparative Recurrent Model for Stereo Matching0
Semantic See-Through Rendering on Light Fields0
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching0
Pyramid Stereo Matching NetworkCode1
Robust Depth Estimation from Auto Bracketed Images0
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model0
Zoom and Learn: Generalizing Deep Stereo Matching to Novel DomainsCode0
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