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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 501517 of 517 papers

TitleStatusHype
Guided Stereo MatchingCode0
Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based MatchingCode0
Digging Into Normal Incorporated Stereo MatchingCode0
ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based RefinementCode0
Fast Feature Extraction with CNNs with Pooling LayersCode0
Road surface 3d reconstruction based on dense subpixel disparity map estimationCode0
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise LabelingCode0
CBMV: A Coalesced Bidirectional Matching Volume for Disparity EstimationCode0
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Depth-Based Selective Blurring in Stereo Images Using Accelerated FrameworkCode0
SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo MatchingCode0
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo MatchingCode0
A Flexible Recursive Network for Video Stereo Matching Based on Residual EstimationCode0
Efficient stereo matching on embedded GPUs with zero-means cross correlationCode0
Unsupervised Cross-spectral Stereo Matching by Learning to SynthesizeCode0
RomniStereo: Recurrent Omnidirectional Stereo MatchingCode0
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatchCode0
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