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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 241–250 of 517 papers

TitleStatusHype
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching—0
SOCRATES: A Stereo Camera Trap for Monitoring of BiodiversityCode0
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks—0
Pseudo-LiDAR for Visual Odometry—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
EASNet: Searching Elastic and Accurate Network Architecture for Stereo MatchingCode0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
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