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Updated: May 13, 2025

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DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields.

Siyu Ren, Junhui Hou, Xiaodong Chen

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    This summary is machine-generated.

    We introduce DirDist, a novel directional distance field (DDF) for 3D geometry. This efficient and robust metric accurately quantifies discrepancies between 3D models, improving various geometric modeling tasks.

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    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • Computer Graphics

    Background:

    • Discrepancy quantification between 3D models (point clouds, meshes) is crucial for many applications.
    • Current methods often struggle with efficiency and effectiveness due to direct correspondence and point-wise aggregation.

    Purpose of the Study:

    • To propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry.
    • To establish a novel approach for quantifying discrepancies between 3D models.

    Main Methods:

    • Developed DirDist based on an implicit representation: the directional distance field (DDF).
    • DDF captures local surface geometry by defining directional distances of 3D points to a model.
    • Transferred model discrepancy to DDF discrepancy on an identical domain, enabling natural correspondence.

    Main Results:

    • DirDist demonstrated superior accuracy across diverse 3D geometric modeling tasks.
    • Evaluated on template surface fitting, rigid/non-rigid registration, scene flow estimation, and human pose optimization.
    • Achieved significantly higher accuracy compared to existing methods.

    Conclusions:

    • DirDist offers a generic and powerful distance metric for 3D geometric data.
    • The proposed method has the potential to significantly advance the field of 3D geometric modeling.
    • Its efficiency, effectiveness, and robustness make it suitable for various downstream applications.