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3D surface reconstruction of transparent objects using laser scanning with a four-layers refinement process.

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    |March 18, 2022
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    This study presents a new method for 3D reconstruction of transparent objects using a laser scanning system. The approach effectively distinguishes and refines first-reflection points, overcoming challenges posed by light transmission for accurate shape recovery.

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    Area of Science:

    • Computer Vision
    • Geometric Reconstruction
    • Optical Metrology

    Background:

    • Reconstructing the 3D geometry of transparent objects is a significant challenge in computer vision.
    • Traditional laser scanning methods struggle with transparent materials due to light transmission and internal reflections.
    • Accurate 3D surface reconstruction is crucial for various applications, including manufacturing, robotics, and augmented reality.

    Purpose of the Study:

    • To develop a fully automatic approach for reconstructing the exterior surface of complex transparent scenes.
    • To address the challenge of distinguishing true surface reflections from internal light transmissions in transparent objects.
    • To improve the accuracy and robustness of 3D reconstruction for transparent and semi-transparent objects.

    Main Methods:

    • A dual-camera system captures scene images from two viewing directions using a line laser scanned by a galvo-mirror.
    • A novel four-layer refinement process is introduced to classify and extract valid first-reflection points from 3D point candidates.
    • Optical geometric constraints, including single-camera validation, dual-camera joint constraints, fusion, and contour-continuity, are employed for point refinement.
    • A new calibration model for the imaging system is proposed to enhance triangulation accuracy.

    Main Results:

    • The proposed method successfully classifies 3D laser point candidates into first-reflection and non-first-reflection points.
    • The four-layer refinement process effectively removes incorrect points caused by light transmission within the object.
    • Experiments demonstrate successful extraction of first-reflection points and recovery of complex shapes for transparent and semi-transparent objects.
    • The inclusion of viewing angle information from the second camera improves reconstruction accuracy compared to traditional methods.

    Conclusions:

    • The developed automatic approach provides a robust solution for the 3D reconstruction of transparent objects.
    • The novel four-layer refinement strategy is key to overcoming the challenges associated with laser scanning transparent surfaces.
    • This method offers a significant advancement in accurately capturing the geometry of complex transparent scenes.