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    UniDepthV2 reconstructs 3D scenes from single images, overcoming domain limitations in monocular metric depth estimation (MMDE). This universal model enhances 3D perception and modeling applicability.

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    Area of Science:

    • Computer Vision
    • 3D Reconstruction
    • Machine Learning

    Background:

    • Monocular metric depth estimation (MMDE) is vital for 3D perception but current methods lack domain generalization.
    • Existing MMDE models perform poorly on unseen data, limiting practical applications.

    Purpose of the Study:

    • To develop a universal monocular metric depth estimation (MMDE) model, UniDepthV2, that generalizes across diverse domains.
    • To enable accurate 3D scene reconstruction from single images without domain-specific training.

    Main Methods:

    • UniDepthV2 employs a self-promptable camera module and a pseudo-spherical output representation to disentangle camera and depth features.
    • Introduced a geometric invariance loss and an edge-guided loss for improved feature invariance and edge sharpness.
    • Utilized a simplified, efficient architecture with an added uncertainty-level output.

    Main Results:

    • UniDepthV2 demonstrates superior zero-shot generalization across ten diverse depth datasets.
    • The model achieves enhanced edge localization and sharpness in metric depth outputs.
    • The uncertainty-level output provides confidence measures for downstream tasks.

    Conclusions:

    • UniDepthV2 offers a universal and flexible solution for monocular metric depth estimation, significantly improving domain generalization.
    • The proposed methods enhance the accuracy, robustness, and applicability of single-image 3D reconstruction.