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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

Updated: Jan 9, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

841

A Behavioral Study of Event-based Depth-Filtered Prosthetic Vision in Simulated Dynamic Environments.

Niklas Hahn, Pehuen Moure, Shih-Chii Liu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new depth filter significantly improved simulated visual prosthesis performance in a road-crossing task, enhancing success rates from 50% to 95%. This advancement aids in developing better visual prostheses for improved artificial vision.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Computer Vision

    Background:

    • Cortical visual prostheses create artificial vision via electrical stimulation, but current electrode arrays are limited by size, restricting visual information.
    • Phosphenes are light perceptions elicited by these prostheses, crucial for restoring sight.

    Purpose of the Study:

    • To investigate a lightweight disparity-based depth filter for improving task performance in simulated phosphene vision.
    • To evaluate the algorithm's effectiveness in a simulated road-crossing task using sighted subjects.
    • To assess if a reinforcement learning (RL) agent can serve as a human surrogate for future algorithm development.

    Main Methods:

    • A behavioral study was conducted with sighted subjects using simulated phosphene vision.
    • A lightweight disparity-based depth filter was applied to extract relevant pixel information from a stereo event camera.
    • Subjects performed a simulated road-crossing task, and their success rates were compared with and without the depth filter.
    • The performance of human subjects was benchmarked against a reinforcement learning (RL) agent.

    Main Results:

    • The depth filter significantly improved the task success rate of subjects from 50% to 95%.
    • Increasing the phosphene count had a minor impact on performance.
    • Subject success rates were only 4.8% lower than the peak accuracy achieved by the RL agent.

    Conclusions:

    • Disparity-based depth filtering is a promising technique for enhancing visual prosthesis performance.
    • The close alignment between human and RL agent performance suggests RL can be valuable for future visual prosthesis algorithm development.
    • This low-compute algorithm is suitable for portable visual prosthesis systems.