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 ◽  
◽  
...  

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.

Author(s):  
Filippo Bergamasco ◽  
Andrea Albarelli ◽  
Luca Cosmo ◽  
Andrea Torsello ◽  
Emanuele Rodola ◽  
...  

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 459
Author(s):  
Hieu Nguyen ◽  
Zhaoyang Wang

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.


2017 ◽  
Vol 34 (8) ◽  
pp. 1763-1781 ◽  
Author(s):  
Haruya Minda ◽  
Norio Tsuda ◽  
Yasushi Fujiyoshi

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.


2018 ◽  
Vol 8 (10) ◽  
pp. 1773 ◽  
Author(s):  
Yanlin He ◽  
Lianqing Zhu ◽  
Guangkai Sun ◽  
Mingxin Yu ◽  
Mingli Dong

Soft actuators are the components responsible for organs and tissues adsorptive fixation in some surgical operations, but the lack of shape sensing and monitoring of a soft actuator greatly limits their application potential. Consequently, this paper proposes a real-time 3D shape reconstruction method of soft surgical actuator which has an embedded optical fiber with two Fiber Bragg Grating (FBG) sensors. First, the design principle and the sensing of the soft actuator based on FBG sensors are analyzed, and the fabrication process of soft actuator which has an embedded optical fiber with two FBG sensors is described. Next, the calibration of the FBG sensors is conducted. Based on curvatures and curve fitting functions, the strategy of 3D shapes reconstruction of the soft actuator is presented. Finally, some bending experiments of the soft actuator are carried out, and the 3D shapes of the soft actuator at different bending states are reconstructed. This well reconstructed 3D shape of a soft actuator demonstrates the effectiveness of the shape reconstruction method that is proposed in this paper, as well as the potential and increased applications of these structures for real soft surgical actuators.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Wang ◽  
Yu Yang ◽  
Baoquan Zhao ◽  
Dazhi Jiang ◽  
Siwei Chen ◽  
...  

In this paper, we introduce a novel 3D shape reconstruction method from a single-view sketch image based on a deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multimodal DNN fusion and is used to segment a given sketch into a series of basic units and build a transformation template by the knots between them. The second module is a nonlinear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtained sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods. Specifically, compared to the state-of-the-art method, the proposed model achieves a performance gain in terms of the five evaluation metrics by an average of 25.5% on the man-made model dataset and 23.4% on the character object dataset using synthetic sketches and by an average of 31.8% and 29.5% on the two datasets, respectively, using human drawing sketches.


2021 ◽  
pp. 102228
Author(s):  
Xiang Chen ◽  
Nishant Ravikumar ◽  
Yan Xia ◽  
Rahman Attar ◽  
Andres Diaz-Pinto ◽  
...  

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