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Reconstruction of transparent objects using phase shifting profilometry based on diffusion models.

Qinghui Zhang, Feng Liu, Lei Lu

|June 11, 2024

Summary

This summary is machine-generated.

This study introduces a novel method using diffusion models to generate non-aliased fringe patterns for 3D reconstruction of transparent objects. This technique overcomes challenges posed by refraction and reflections, enabling accurate 3D shape measurement.

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Area of Science:

Optics and Photonics
Computer Vision
3D Metrology

Background:

Phase shifting profilometry is crucial for 3D reconstruction of diffuse surfaces.
Transparent objects pose significant challenges due to optical phenomena like refraction and multiple reflections, leading to pattern aliasing.
Existing methods struggle with accurate 3D measurement of transparent objects.

Purpose of the Study:

To analyze the formation of aliasing fringe patterns in transparent objects.
To propose a novel method for generating non-aliased fringe patterns for transparent object reconstruction.
To reconstruct the 3D geometry of transparent objects using conventional structured light techniques.

Main Methods:

Analysis of aliasing fringe pattern formation in transparent objects.
Utilizing diffusion models to learn front surface light intensity distribution.
Generating non-aliased fringe patterns reflected solely from the front surface.
Reconstructing 3D shapes using conventional structured light with generated patterns.

Main Results:

Successfully generated non-aliased fringe patterns for transparent objects.
Demonstrated the feasibility and performance of the proposed method on unseen transparent object data.
Achieved accurate 3D shape reconstruction of transparent objects.

Conclusions:

The proposed diffusion model-based approach effectively generates non-aliased fringe patterns for transparent object 3D reconstruction.
The method shows generalization capabilities to local-transparent and translucent objects.
This work highlights the potential of learnable diffusion-based frameworks for transparent object metrology.