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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
M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World0
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo MatchingCode0
ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based RefinementCode0
Consistency-aware Self-Training for Iterative-based Stereo Matching0
Distilling Stereo Networks for Performant and Efficient Leaner NetworksCode0
LeanStereo: A Leaner Backbone based Stereo NetworkCode0
RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization0
Stereo Any Video: Temporally Consistent Stereo Matching0
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking0
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