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Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning

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Abstract

Single-shot 3D shape reconstruction integrating structured light and deep learning has drawn considerable attention and achieved significant progress in recent years due to its wide-ranging applications in various fields. The prevailing deep-learning-based 3D reconstruction using structured light generally transforms a single fringe pattern to its corresponding depth map by an end-to-end artificial neural network. At present, it remains unclear which kind of structured-light patterns should be employed to obtain the best accuracy performance. To answer this fundamental and much-asked question, we conduct an experimental investigation of six representative structured-light patterns adopted for single-shot 2D-to-3D image conversion. The assessment results provide a valuable guideline for structured-light pattern selection in practice.

© 2022 Optica Publishing Group

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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