Three-dimensional surface reconstruction via a robust binary shape-coded structured light method

Suming Tang; Xu Zhang; Zhan Song; Hualie Jiang; Lei Nie

doi:10.1117/1.OE.56.1.014102

6 January 2017 Three-dimensional surface reconstruction via a robust binary shape-coded structured light method

Suming Tang, Xu Zhang, Zhan Song, Hualie Jiang, Lei Nie

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Optical Engineering, Vol. 56, Issue 1, 014102 (January 2017). https://doi.org/10.1117/1.OE.56.1.014102

Abstract

A binary shape-coded structured light method for single-shot three-dimensional reconstruction is presented. The projected structured pattern is composed with eight geometrical shapes with a coding window size of 2×2. The pattern element is designed as rhombic with embedded geometrical shapes. The pattern feature point is defined as the intersection of two adjacent rhombic shapes, and a multitemplate-based feature detector is presented for its robust detection and precise localization. Based on the extracted grid-points, a topological structure is constructed to separate the pattern elements from the obtained image. In the decoding stage, a training dataset is first established from training samples that are collected from a variety of target surfaces. Then, the deep neural network technique is applied for the classification of pattern elements. Finally, an error correction algorithm is introduced based on the epipolar and neighboring constraints to refine the decoding results. The experimental results show that the proposed method not only owns high measurement precision but also has strong robustness to surface color and texture.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Suming Tang, Xu Zhang, Zhan Song, Hualie Jiang, and Lei Nie "Three-dimensional surface reconstruction via a robust binary shape-coded structured light method," Optical Engineering 56(1), 014102 (6 January 2017). https://doi.org/10.1117/1.OE.56.1.014102

Received: 26 September 2016; Accepted: 16 December 2016; Published: 6 January 2017

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