Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network
Hieu Nguyen, Zhaoyang Wang
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.
Topics & Concepts
Computer scienceArtificial intelligenceStructured lightRobustness (evolution)Computer visionConvolutional neural networkGrayscaleDeep learningIterative reconstruction3D reconstructionImage (mathematics)Pattern recognition (psychology)BiochemistryChemistryGeneOptical measurement and interference techniquesAdvanced Vision and ImagingImage Processing Techniques and Applications