SOTAVerified

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 221230 of 517 papers

TitleStatusHype
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images0
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks0
Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based MatchingCode0
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense ReconstructionCode1
TriStereoNet: A Trinocular Framework for Multi-baseline Disparity EstimationCode1
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching0
3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks0
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems0
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo MatchingCode1
Show:102550
← PrevPage 23 of 52Next →

No leaderboard results yet.