Stereo Disparity Estimation
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Benchmarks
Most implemented
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
Neural Markov Random Field for Stereo Matching
Papers
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in ste…
Stereo Disparity EstimationStereo Depth Estimation3D ReconstructionDiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning
Depth estimation is crucial for intelligent systems, enabling applications from autonomous navigation to augmented reality. While traditional stereo and active depth sensors have limitations in cost, power, and robustnes…
Autonomous NavigationDepth EstimationDepth PredictionDisparity Estimation+2EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency
Event cameras have the potential to revolutionize the field of robot vision, particularly in areas like stereo disparity estimation, owing to their high temporal resolution and high dynamic range. Many studies use deep l…
Disparity EstimationStereo Disparity EstimationMoCha-Stereo: Motif Channel Attention Network for Stereo Matching
Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel generation process, resulting in edge detai…
Disparity EstimationStereo Depth EstimationStereo Disparity EstimationStereo MatchingNeural Markov Random Field for Stereo Matching
Stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods, the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accurac…
Domain GeneralizationInductive BiasStereo Disparity EstimationStereo Matching+1An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar s…
3D geometry3D Scene ReconstructionDisparity EstimationStereo Disparity Estimation