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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 131140 of 517 papers

TitleStatusHype
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets0
Multi-scale Alternated Attention Transformer for Generalized Stereo Matching0
ELFNet: Evidential Local-global Fusion for Stereo MatchingCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal0
Adaptive Multi-Modal Cross-Entropy Loss for Stereo MatchingCode1
Continuous Cost Aggregation for Dual-Pixel Disparity Extraction0
StereoVAE: A lightweight stereo-matching system using embedded GPUs0
MVPSNet: Fast Generalizable Multi-view Photometric StereoCode1
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