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Temporal Stereo Matching From Event Cameras via Joint Learning With Stereoscopic Flow.

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    This study introduces a temporal event stereo framework using event cameras for 3D environment perception. The novel approach enhances stereo matching by integrating past data, achieving state-of-the-art performance on multiple datasets.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Technology

    Background:

    • Event cameras, inspired by the retina, offer high dynamic range, temporal resolution, and low power consumption.
    • They excel in perceiving 3D environments under challenging conditions due to continuous, detailed pixel movement recording.
    • Leveraging the temporal density of event data is crucial for advanced perception tasks.

    Purpose of the Study:

    • To develop a temporal event stereo framework that utilizes past information for enhanced 3D environment perception.
    • To improve stereo matching accuracy by integrating temporal data from event cameras.
    • To demonstrate the computational efficiency of the proposed method.

    Main Methods:

    • Introduced a temporal event stereo framework integrating past event data.
    • Jointly trained an event stereo matching network with stereoscopic flow.
    • Trained motion flows using disparity maps instead of optical flow ground truth.
    • Employed cascading stacking of past data for computational efficiency.

    Main Results:

    • Achieved state-of-the-art stereo matching performance on MVSEC, DSEC, M3ED, and EVIMO2 datasets.
    • Demonstrated significant performance boost through temporal aggregation of information via stereoscopic flow.
    • Showcased computational efficiency in processing event data.

    Conclusions:

    • The temporal event stereo framework effectively leverages past information for robust 3D perception.
    • The method offers a significant advancement in event-based stereo matching.
    • This approach presents a computationally efficient solution for real-time applications.