Photogrammetry-Based 3D Printing Reproduction Method for Oil Paintings

2017 ◽  
Vol 32 (03) ◽  
pp. 1854007 ◽  
Author(s):  
Chen Chen ◽  
Songhua He ◽  
Guangxue Chen ◽  
Hui Cao
Keyword(s):  
Image Segmentation ◽  
3D Printing ◽  
3D Models ◽  
Geometric Information ◽  
Oil Painting ◽  
Oil Paintings ◽  
Control Mesh ◽  
Tracing Algorithm ◽  
Reproduction Method ◽  
Image Segmentation Algorithm

This paper proposes a new oil painting reproduction method using 3D printing to compensate for the deficiencies of the existing methods. First, 3D reconstruction of oil paintings is completed by photogrammetry; the oil painting color and the 3D geometric information are recovered better by acquiring several sets of orthophotomaps, and modeling accuracy is ensured with a control mesh or by flattening. Next, the contours and hypsometric tints of the 3D model for oil paintings are generated using contour tracing algorithm, and the image segmentation of renderings is completed using RGB image segmentation algorithm, with the layered section extracted from each layer and the 3D geometric information converted into 2D plane information. Finally, the 3D models of oil paintings are presented through UV inkjet printing with images superimposed layer upon layer, and stereoscopic reproduction of oil paintings is completed based on the orthophotomaps printed from the 3D models.

scholarly journals 3D Printing of Oil Paintings Based on Material Jetting and Its Reduction of Staircase Effect

Polymers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2536 ◽  
Author(s):  
Jiangping Yuan ◽  
Chen Chen ◽  
Danyang Yao ◽  
Guangxue Chen
Keyword(s):  
3D Printing ◽  
Visual Assessment ◽  
Color Difference ◽  
Oil Painting ◽  
Uv Curable ◽  
Oil Paintings ◽  
Layer Sequence ◽  
3D Objects ◽  
Staircase Effect ◽  
Printing Processes

Material jetting is a high-precision and fast 3D printing technique for color 3D objects reproduction, but it also suffers from color accuracy and jagged issues. The UV inks jetting processes based on the polymer jetting principle have been studied from printing materials regarding the parameters in the default layer order, which is prone to staircase effects. In this work, utilizing the Mimaki UV inks jetting system with a variable layer thickness, a new framework to print a photogrammetry-based oil painting 3D model has been proposed with the tunable coloring layer sequence to improve the jagged challenge between adjacent layers. Based on contour tracking, a height-rendering image of the oil painting model is generated, which is further segmented and pasted to the corresponding slicing layers to control the overall printing sequence of coloring layers and white layers. The final results show that photogrammetric models of oil paintings can be printed vividly by UV-curable color polymers, and that the proposed reverse-sequence printing method can significantly improve the staircase effect based on visual assessment and color difference. Finally, the case of polymer-based oil painting 3D printing provides new insights for optimizing color 3D printing processes based on other substrates and print accuracy to improve the corresponding staircase effect.

A New Method for Reproducing Oil Paintings Based on 3D Printing

2014 ◽  
Vol 644-650 ◽  
pp. 2386-2389 ◽  
Author(s):  
Chen Chen ◽  
Guang Xue Chen ◽  
Zhao Hui Yu ◽  
Zhao Hui Wang
Keyword(s):  
3D Printing ◽  
Surface Morphology ◽  
3D Model ◽  
Laser Scanner ◽  
New Method ◽  
Oil Painting ◽  
Oil Paintings ◽  
Ink Jet

This work aims at presenting a new method for reproducing oil paintings via 3D printing. We used a laser scanner to detect the surface morphology of an oil painting and found it was capable to print the stereo brushstrokes through layered printing after analyzing the UV ink thickness of type UJF-3024 ink-jet printer. Base on the 3D model we established, a slicing strategy was proposed for 3D printing. An experiment was conducted to validate feasibility of the method. Ultimately, the method has proven to be comparatively effective.

scholarly journals Application of 3D-Modeling in Medicine in Preparation for 3D-Printing

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nataliya Yuriivna Onykiyenko
Keyword(s):  
Image Segmentation ◽  
Comparative Analysis ◽  
3D Printing ◽  
3D Modeling ◽  
Mesh Refinement ◽  
3D Models ◽  
Medical Field ◽  
Segmented Model ◽  
Polygon Mesh ◽  
Modeling Software

3D-modeling in the medical field can be used to create medical models (eg, tissues and human organs) using 3D-printing or used for digital 3D visualization of the necessary structures. Medical 3D-printing is used when the work on prostheses that should perfectly match the patient's anatomy is needed. In addition, thanks to 3D-modeling technology, it is possible to develop peculiar medical tools. It is also possible to perform trial surgeries on 3D-models before the actual operation. There is special software for creating medical 3D-models for further printing. The purpose of this work is to determine the functions of 3D-modeling in preparation for 3D-printing in the process of creating medical models and comparative analysis of software for 3D-modeling used in the medical field. There is a common workflow that can be used to convert volumetric medical imaging data (created by computer tomography (CT), or other imaging techniques) into physical models printed on a 3D-printer. This process is divided into three stages: image segmentation, polygon mesh refinement, and 3D-printing. 3D-modeling programs are used at the stage of polygon mesh refinement. They allow almost unlimited manipulations to refine the mesh to make the model printable. The main manipulations for post-processing of a segmented model using 3D-modeling are: 1) reparation - correction of errors and discrepancies that sometimes occur in the process of segmentation and images export; 2) smoothing - correction of errors that occur during segmentation due to inappropriate resolution of the original medical image via softening by smoothing the surface of the model; 3) adding elements - combining a segmented model with other structures or removing unnecessary parts from the segmentation. As a result of a comparative analysis of 3D-modeling software used in the medical field, it was found that for 3D-modeling can be used software specifically designed for medical 3D-modeling and regular 3D-modeling software. When using regular software, you need third-party software to get the correct model file format. The choice of software depends on the goal: to work with implants and create patient-specific devices, it is possible to use specially designed programs for these purposes, such as Within Medical and Medical Design Studio; if high accuracy is required, it is possible to use D2P created for working with DICOM-images at the image segmentation stage; to achieve fast results, when maintaining of maximum accuracy is not needed, a mobile version of the software, such as Ossa 3D, can be used; common 3D-modeling software, such as Cinema 4D and Blender, can be used to develop peculiar tools and medical equipment.

scholarly journals MeshSlicer: A 3D-Printing software for printing 3D-models with a 6-axis industrial robot

Procedia CIRP ◽  
2021 ◽  
Vol 99 ◽  
pp. 110-115
Author(s):  
Jan Werner ◽  
Mohamed Aburaia ◽  
Alexander Raschendorfer ◽  
Maximilian Lackner
Keyword(s):  
3D Printing ◽  
Industrial Robot ◽  
3D Models

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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