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

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 459
Author(s):  
Hieu Nguyen ◽  
Zhaoyang Wang
Keyword(s):  
Neural Network ◽  
Structured Light ◽  
Three Dimensional ◽  
Industrial Applications ◽  
Shape Reconstruction ◽  
Reconstruction Technique ◽  
Neural Network Learning ◽  
3D Shape ◽  
Triple Frequency ◽  
3D Shape Reconstruction

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.

scholarly journals Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view

2019 ◽  
Vol 13 (13) ◽  
pp. 2457-2466 ◽  
Author(s):  
Patricio Rivera ◽  
Edwin Valarezo Añazco ◽  
Mun-Taek Choi ◽  
Tae-Seong Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shape Reconstruction ◽  
3D Shape ◽  
3D Shape Reconstruction ◽  
Depth View

Digital Shape Reconstruction of a Micro-Sized Machining Tool Using Light-Field Microscopy

2016 ◽  
Vol 10 (2) ◽  
pp. 172-178 ◽  
Author(s):  
Shin Usuki ◽  
◽  
Masaru Uno ◽  
Kenjiro T. Miura ◽  
◽  
...  
Keyword(s):  
Light Field ◽  
Three Dimensional ◽  
Microlens Array ◽  
Axial Direction ◽  
Shape Reconstruction ◽  
Reconstruction Method ◽  
Actual Length ◽  
3D Shape ◽  
Depth Images ◽  
3D Shape Reconstruction

In this paper, we propose a digital shape reconstruction method for micro-sized 3D (three-dimensional) objects based on the shape from silhouette (SFS) method that reconstructs the shape of a 3D model from silhouette images taken from multiple viewpoints. In the proposed method, images used in the SFS method are depth images acquired with a light-field microscope by digital refocusing (DR) of a stacked image along the axial direction. The DR can generate refocused images from an acquired image by an inverse ray tracing technique using a microlens array. Therefore, this technique provides fast image stacking with different focal planes. Our proposed method can reconstruct micro-sized object models including edges, convex shapes, and concave shapes on the surface of an object such as micro-sized defects so that damaged structures in the objects can be visualized. Firstly, we introduce the SFS method and the light-field microscope for 3D shape reconstruction that is required in the field of micro-sized manufacturing. Secondly, we show the developed experimental equipment for microscopic image acquisition. Depth calibration using a USAF1951 test target is carried out to convert relative value into actual length. Then 3D modeling techniques including image processing are implemented for digital shape reconstruction. Finally, 3D shape reconstruction results of micro-sized machining tools are shown and discussed.

scholarly journals Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3718 ◽  
Author(s):  
Hieu Nguyen ◽  
Yuzeng Wang ◽  
Zhaoyang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
3D Imaging ◽  
Structured Light ◽  
Depth Map ◽  
Shape Reconstruction ◽  
Single Shot ◽  
Deep Convolutional Neural Networks ◽  
3D Shape ◽  
3D Shape Reconstruction

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.

MIMONet: Structured-light 3D shape reconstruction viamulti-input multi-output network

2021 ◽  
Author(s):  
Hieu Nguyen ◽  
Khanh Ly ◽  
Thanh Nguyen ◽  
Yuzeng Wang ◽  
Zhaoyang Wang
Keyword(s):  
Structured Light ◽  
Shape Reconstruction ◽  
3D Shape ◽  
3D Shape Reconstruction

scholarly journals hNet: Single-shot 3D shape reconstruction using structured light and h-shaped global guidance network

2021 ◽  
pp. 100104
Author(s):  
Hieu Nguyen ◽  
Khanh L. Ly ◽  
Tan Tran ◽  
Yuzheng Wang ◽  
Zhaoyang Wang
Keyword(s):  
Structured Light ◽  
Shape Reconstruction ◽  
Single Shot ◽  
3D Shape ◽  
3D Shape Reconstruction ◽  
Global Guidance

Three-Dimensional Shape and Fall Velocity Measurements of Snowflakes Using a Multiangle Snowflake Imager

2017 ◽  
Vol 34 (8) ◽  
pp. 1763-1781 ◽  
Author(s):  
Haruya Minda ◽  
Norio Tsuda ◽  
Yasushi Fujiyoshi
Keyword(s):  
Three Dimensional ◽  
Shape Reconstruction ◽  
Velocity Measurements ◽  
Particle Size Distributions ◽  
Fall Velocity ◽  
Silhouette Width ◽  
3D Shape ◽  
3D Shape Reconstruction ◽  
Dimensional Shape ◽  
Three Dimensional Shape

AbstractThis paper describes a Multiangle Snowflake Imager (MSI) designed to capture the pseudo-three-dimensional (3D) shape and the fall velocity of individual snowflakes larger than 1.5 mm in size. Four height-offset line-image scanners estimate fall velocities and the four-angle silhouettes are used to reconstruct the 3D snowflake shapes. The 3D shape reconstruction is tested using reference objects (spheres, spheroids, cubes, and plates). The four-silhouette method of the MSI improves the representation of the particle shape and volume compared to two-silhouette methods, such as the two-dimensional video disdrometer (2DVD). The volume (equivolumetric diameters) of snowflakes estimated by the four-silhouette method is approximately 44% (13%) smaller than that estimated by the two-silhouette method. The ability of the imager to measure the fall velocity and particle size distributions based on the silhouette width and the equivolumetric diameter of 3D-shaped particles is verified via a comparison with the 2DVD in three snowfall events.

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