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A Study on the Generality of Neural Network Structures for Monocular Depth Estimation.

Jinwoo Bae, Kyumin Hwang, Sunghoon Im

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    Transformers show shape-bias, improving monocular depth estimation generalization, unlike CNNs which exhibit texture-bias. This research analyzes backbone networks for better out-of-distribution performance in depth estimation tasks.

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    Area of Science:

    • Computer Vision
    • Deep Learning

    Background:

    • Monocular depth estimation is crucial for autonomous systems.
    • Current models show performance gains but lack generalization analysis.
    • Existing evaluations often use limited benchmark datasets.

    Purpose of the Study:

    • Investigate backbone network generalization in monocular depth estimation.
    • Analyze internal representations of CNNs and Transformers.
    • Identify biases (shape vs. texture) affecting model performance.

    Main Methods:

    • Evaluated state-of-the-art models on in-distribution and out-of-distribution datasets.
    • Analyzed intermediate layer representations using synthetic texture-shifted data.
    • Conducted dense ablation studies on various backbone networks.

    Main Results:

    • Transformers exhibit strong shape-bias; CNNs exhibit strong texture-bias.
    • Texture-biased models generalize poorly compared to shape-biased models.
    • CNNs' locality induces texture-bias; Transformers' self-attention induces shape-bias.

    Conclusions:

    • Shape-biased models are superior for monocular depth estimation generalization.
    • Backbone network architecture significantly influences bias and performance.
    • Findings are validated on real-world driving datasets.