• 2015-3DIPM-Stereo matching with space-constrained cost aggregation and segmentation-based disparity refinement
• 2015-IVC-A Stereo Matching Approach Based on Particle Filters and Scattered Control Landmarks
• 2015-arXiv-Continuous stereo matching using local expansion moves
• 2015-CVPR-Displets Resolving Stereo Ambiguities using Object Knowledge
• 2015-CVPR-Object Scene Flow for Autonomous Vehicles
• 2015-CVPR-Recursive Edge-Aware Filters for Stereo Matching
• 2015-ICCV-A Global Stereo Model with Mesh Alignment Regularization for
• 2015-ICCV-Segment Graph Based Image Filtering Fast Structure-Preserving Smoothing
• 2015-IJCV-3D Scene Flow Estimation with a Piecewise Rigid Scene Model
• 2015-IV-A Multi-Block-Matching Approach for Stereo
• 2015-IVC-Enhanced disparity estimation in stereo images
• 2015-LCVB-Luminance, colour, viewpoint and border enhanced disparity energy model
• 2015-LNCS-Graph cuts stereo matching based on Patch-Match and ground control points constraint.
• 2015-TCSVT-Accurate Image-guided Stereo Matching with Efficient Matching Cost and Disparity Refinement-proof
• 2015-TIP-PM-PM PatchMatch With Potts Model for Object Segmentation and Stereo Matching
• 2016-arXiv-A Continuous Optimization Approach
• 2016-arXIv-Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
• 2016-arXIv-Detect, Replace, Refine Deep Structured Prediction For Pixel Wise Labeling
• 2016-arXiv-Embedded Real-time Stereo Estimation via Semi-Global Matching on the GPU
• 2016-arXIv-Improved Stereo Matching with Constant Highway Networks
• 2016-CVPR-A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• 2016-CVPR-Coordinating Multiple Disparity Proposals for Stereo Computation
• 2016-CVPR-Efficient Deep Learning for Stereo Matching
• 2016-GCPR-A Prediction-correction Approach for Real-Time Optical Flow Computation Using Stereo
• 2016-JCVIR-Stereo Matching Algorithm based on Perpixel Difference Adjustment, Iterative Guided Filter and Graph Segmentation
• 2016-JMLR-Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
• 2016-JVCIR-Convolutional Nueral Network based Deep Conditional Random Fields for Stereo Matching
• 2016-XXX-Patch Based Confidence Prediction for Dense Disparity Map
• 2017-arXiv-End-to-End Learning of Geometry and Context for Deep Stereo Regression