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Textureless Surface Feature Point Detection via Micro-Geometry Reconstruction.

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    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 7, 2026
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    This study introduces a new computer vision method for detecting feature points on textureless surfaces by analyzing micro-geometry. The approach reconstructs surface structures from RGB images, enabling stable feature extraction without specialized equipment.

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    Area of Science:

    • Computer Vision
    • Computational Geometry
    • Optical Metrology

    Background:

    • Feature point detection on textureless surfaces is challenging due to lack of visual cues.
    • Micro-geometry offers stable but often overlooked information for feature extraction.

    Purpose of the Study:

    • To develop a novel feature point detection method for textureless surfaces using only a single RGB image.
    • To leverage reconstructed micro-geometry for robust feature characterization.

    Main Methods:

    • Modeling light-surface interactions to analyze phase modulation in reflected light.
    • Reconstructing micro-geometry via Gabor Kernel-based spectral analysis.
    • Developing a Concave-Convex Index (CCI) for geometry-aware feature description.

    Main Results:

    • Successfully reconstructed micro-geometry and quantified surface height variations.
    • Achieved stable and repeatable feature point extraction on challenging surfaces.
    • Demonstrated superior performance on TUM, T-LESS, and Shape2.5D datasets.

    Conclusions:

    • The proposed method reliably detects feature points on textureless surfaces without specialized equipment.
    • Micro-geometry analysis provides a robust alternative for feature detection in computer vision.
    • The Concave-Convex Index (CCI) offers a novel geometric descriptor for stable feature characterization.