Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Hieu Nguyen,Zhaoyang Wang

doi:10.3390/photonics8110459

Abstract

Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.

Highlights

Three-dimensional (3D) shape or depth perception has been a favored long-term research topic in recent decades, driven by numerous scientific and engineering applications in many fields, due to its capability of perceiving depth information that cannot be fulfilled by two-dimensional (2D) imaging
The proposed fringe-to-fringe network transforms a single RGB color fringe pattern into multiple phaseshifted grayscale fringe patterns demanded by the subsequent 3D shape reconstruction process
The characteristics of single-shot input can help to substantially reduce the capturing time; the accuracy can be maintained since the final 3D reconstruction is based on the conventional state-of-the-art high-accuracy algorithm

Summary

Introduction

Three-dimensional (3D) shape or depth perception has been a favored long-term research topic in recent decades, driven by numerous scientific and engineering applications in many fields, due to its capability of perceiving depth information that cannot be fulfilled by two-dimensional (2D) imaging. The 3D shape and depth perception techniques are typically stereo vision- and optics-based, and the most widely used scheme involves employing structured light to facilitate image analysis. A fringe pattern-based technique generally requires capturing multiple fringe-shifted images in sequence to achieve high accuracy, but the measurement speed is slow. A speckle pattern-based technique usually uses two images simultaneously captured by two cameras; in this way, real-time measurement speed can be attained, but the corresponding accuracy is often relatively low. A structured-light technique capable of providing high-accuracy and fast-speed performance is always of great interest in the research and development of new 3D scanning systems

Methods

Results

Conclusion