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    This study defines point cloud normals for feature points, enabling multiple normals per point. A novel feature-preserving normal estimation method is introduced, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • 3D Data Processing

    Background:

    • Feature points in point clouds, where surfaces intersect, have ambiguous normals.
    • Existing normal estimation methods struggle with feature points, leading to inaccuracies.

    Purpose of the Study:

    • To propose a unified definition for point cloud normals, including feature points.
    • To develop a feature-preserving normal estimation method that handles ambiguity.

    Main Methods:

    • A unified definition allowing multiple normals for feature points.
    • A feature-preserving normal estimation method using a pair consistency voting scheme.
    • An adaptive strategy to address sampling anisotropy and an error measure for evaluation.

    Main Results:

    • The proposed method successfully estimates multiple normals for feature points.
    • The method demonstrates superior performance in feature preservation compared to state-of-the-art techniques.
    • A comprehensive benchmark dataset for normal estimation was created and utilized.

    Conclusions:

    • The unified definition and novel estimation method provide accurate and feature-preserving normals for point clouds.
    • This work offers a significant advancement in 3D data processing and analysis.
    • The introduced benchmark facilitates future research in normal estimation.