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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 351–400 of 517 papers

TitleStatusHype
Stereo Object Matching Network—0
Stereo on a budget—0
Stereo Risk: A Continuous Modeling Approach to Stereo Matching—0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images—0
StereoVAE: A lightweight stereo-matching system using embedded GPUs—0
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks—0
Structural inference affects depth perception in the context of potential occlusion—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
Sub-pixel matching method for low-resolution thermal stereo images—0
Superpixel Cost Volume Excitation for Stereo Matching—0
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking—0
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint—0
The Global Patch Collider—0
The Sampling-Gaussian for stereo matching—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework—0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos—0
Tree-based iterated local search for Markov random fields with applications in image analysis—0
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
UASNet: Uncertainty Adaptive Sampling Network for Deep Stereo Matching—0
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems—0
Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning—0
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching—0
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity—0
Unsupervised Learning of Stereo Matching—0
Unsupervised monocular stereo matching—0
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model—0
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching—0
VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors—0
Virtual Blood Vessels in Complex Background using Stereo X-ray Images—0
Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching—0
Visually Imbalanced Stereo Matching—0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation—0
WaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching—0
Wide baseline stereo matching with convex bounded-distortion constraints—0
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints—0
Widening siamese architectures for stereo matching—0
One-view occlusion detection for stereo matching with a fully connected CRF model—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline—0
Open-World Stereo Video Matching with Deep RNN—0
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Parameterized Cost Volume for Stereo Matching—0
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