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Related Experiment Video

Updated: Jul 16, 2026

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

BSEF-Stereo: A Stereo Matching Model Based on Branching Strategy and Error Feedback.

Kaicheng Li¹, Jinlong Yang¹, Chin Chi Choi²

¹School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

BSEF-Stereo improves stereo matching by adaptively updating disparity maps and refining errors. This novel framework enhances accuracy in challenging areas like object boundaries and repetitive patterns.

Keywords:

branching strategy disparity refinement error feedback iterative optimization stereo matching

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Published on: January 14, 2020

Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Stereo matching is difficult in areas with few textures, repetitive patterns, occlusions, and object edges.
Current methods struggle to balance local detail preservation with broad contextual reasoning.

Purpose of the Study:

To introduce BSEF-Stereo, an iterative framework for enhanced stereo matching.
To address limitations of existing recurrent updaters in handling complex scene elements.

Main Methods:

Utilizes a channel-position attention module to enhance spatial and channel cues.
Employs a gated recurrent unit with complementary branches for detail preservation and context aggregation.
Incorporates an error-aware refinement module using reprojection error and image guidance.

Main Results:

Achieved competitive performance on Scene Flow, KITTI 2012, KITTI 2015, and Middlebury datasets.
BSEF-Stereo obtained 0.41 px EPE and 2.27% D1 on Scene Flow.
Reached a D1-all error of 1.48% on KITTI 2015.

Conclusions:

BSEF-Stereo effectively combines adaptive updating and error feedback for robust stereo matching.
The proposed modules contribute significantly to performance across diverse scenes.
The framework demonstrates state-of-the-art results in challenging stereo vision tasks.