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Updated: Jul 25, 2025

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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Monocular Road Planar Parallax Estimation.

Haobo Yuan, Teng Chen, Wei Sui

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 29, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We introduce RPANet, a novel deep learning network for 3D scene reconstruction from monocular images. It leverages road plane geometry and planar parallax for accurate depth estimation in autonomous driving.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Deep Learning

    Background:

    • 3D structure estimation is vital for autonomous driving.
    • Existing methods like LiDAR are costly, while depth prediction lacks geometric context.

    Purpose of the Study:

    • To propose a novel deep neural network, RPANet, for accurate 3D sensing from monocular image sequences.
    • To utilize planar parallax and road plane geometry for improved 3D reconstruction.

    Main Methods:

    • Developed Road Planar Parallax Attention Network (RPANet).
    • Input: Image pairs aligned by road plane homography.
    • Output: γ map (height-to-depth ratio) for 3D reconstruction.
    • Introduced a novel cross-attention module to enhance parallax perception.

    Main Results:

    • Demonstrated effectiveness of RPANet on Waymo Open Dataset.
    • Achieved high 3D reconstruction accuracy in challenging driving scenarios.
    • Validated the utility of planar parallax for geometric scene understanding.

    Conclusions:

    • RPANet offers an effective, geometry-aware approach to 3D sensing from monocular sequences.
    • The method successfully leverages planar parallax for accurate depth and structure estimation.
    • This approach provides a cost-effective alternative for autonomous driving perception systems.