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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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DENAO: Monocular Depth Estimation Network With Auxiliary Optical Flow.

Jingyu Chen, Xin Yang, Qizeng Jia

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 7, 2020
    PubMed
    Summary

    This study introduces DENAO, a novel method for estimating depth from multi-view images. DENAO uses a convolutional neural network (CNN) with optical flow and epipolar geometry for faster and more accurate depth estimation.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Deep Learning

    Background:

    • Estimating depth from multi-view images is crucial for autonomous systems.
    • Existing methods often face limitations in speed and accuracy.

    Purpose of the Study:

    • To develop an efficient and accurate depth estimation method using multi-view images.
    • To improve both depth estimation and optical flow prediction concurrently.

    Main Methods:

    • A novel architecture, DENAO, combining a convolutional neural network (CNN) for depth estimation with an auxiliary optical flow network.
    • Utilizing epipolar geometry constraints and tightly-coupled encoder-decoder networks with inter-network exchange blocks.
    • The architecture supports an arbitrary number of multi-view images with scalable computational cost.

    Main Results:

    • DENAO achieves high performance on five public datasets.
    • The method runs at 38.46fps on a single Nvidia TITAN Xp GPU, significantly outperforming state-of-the-art methods in speed (5.15X–142X faster).
    • DENAO concurrently outputs accurate depth and optical flow predictions, matching or exceeding existing specialized methods.

    Conclusions:

    • The proposed DENAO method offers a significant advancement in multi-view depth estimation.
    • Its efficiency and accuracy make it suitable for real-time robotic and computer vision applications.
    • The concurrent prediction of depth and optical flow provides added value for complex scene understanding.