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Related Experiment Video

Updated: Jun 18, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

High-Performance Binocular Disparity Prediction Algorithm for Edge Computing.

Yuxi Cheng¹, Yang Song¹, Yi Liu¹

¹Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This study introduces a new disparity estimation algorithm for edge devices, significantly improving accuracy and reducing computational load. The novel approach enhances structural adaptability and practicability for real-world applications.

Keywords:

3D convolution activation function binocular disparity edge computing practical

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

End-to-end disparity estimation algorithms face challenges in structural adaptation and accuracy on edge devices.
Existing methods often struggle with computational complexity and parameter count for efficient deployment.

Purpose of the Study:

To propose a novel disparity calculation algorithm for enhanced accuracy and efficiency on edge neural network accelerators.
To address structural adaptation issues and reduce computational complexity in disparity estimation.

Main Methods:

Utilized low-rank approximation to replace 3D convolution and transposed 3D convolution.
Incorporated WReLU activation function to mitigate data compression.
Employed unimodal cost volume filtering and a confidence estimation network for cost volume regularization.

Main Results:

Achieved a 38.3% reduction in absolute error compared to typical networks.
Reduced the three-pixel error to 1.41%.
Decreased the number of parameters by 67.3% while improving accuracy.

Conclusions:

The proposed algorithm offers superior accuracy and reduced computational complexity for disparity estimation.
Demonstrates strong structural adaptability and practicability, making it easier to deploy on edge devices.