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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 231240 of 517 papers

TitleStatusHype
A Learned Stereo Depth System for Robotic Manipulation in Homes0
Survey on Semantic Stereo Matching / Semantic Depth Estimation0
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
Learning Signed Distance Field for Multi-view Surface ReconstructionCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D DetectorCode1
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume ExcitationCode1
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones0
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