Manual 3D Reconstruction from Multiple Images, Part 1: Principles

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Correspondence to Yiwei Zhang. This work is licensed under a Creative Commons Attribution 4. Micro and Nano Systems Letters Frontiers in Robotics and AI Journal of Biomedical Optics By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Article metrics. Advanced search. Skip to main content.

Subjects Imaging and sensing Imaging techniques Optical physics. Abstract Photometric stereo is a three dimensional 3D imaging technique that uses multiple 2D images, obtained from a fixed camera perspective, with different illumination directions. Introduction Three dimensional 3D image reconstruction is a procedure of creating a mathematical representation of a 3D object. Figure 1: System setup. Full size image. Figure 2: Program interface. Figure 3: Reconstruction program pipeline.

Figure 4: Reconstruction results of objects: a hemisphere, an arc, and a mannequin head. Table 1: Deviations between measured values and true values. Full size table. Figure 5: 3D image reconstruction of a real human subject. Methods Photometric Stereo The appearance of an object in a photo results from the effects of illumination, object orientation, object shape and its reflectance.

Figure 6: Photometric stereo. Additional Information How to cite this article : Zhang, Y. References 1. Google Scholar 2. Article Google Scholar 3. Google Scholar 4. Google Scholar 6. Google Scholar 7. Google Scholar 8. Article Google Scholar 9. Google Scholar PubMed Article Google Scholar Article Google Scholar Gibson , Rebecca Hay , Miles J. Supplementary information PDF files 1. Supplementary Information. Rights and permissions This work is licensed under a Creative Commons Attribution 4. About this article Publication history Received 31 December Accepted 29 April Published 09 June Agochukwu , Ahmed A.

There are some parameters that could be used to reduce the errors in the reconstructed 3D shape. In the SFS method, we used a pixel size of The reconstructed 3D model could be smoother and finer if a smaller voxel size was used. The number of CCD camera elements also affected the quality of the silhouette images. Using a higher resolution camera could also reduce the minimum pixel size of the silhouette images and make the visual cone more precise.

Additionally, the magnification and depth of field of the imaging lens are important parameters. Since there is a trade-off relationship between the magnification and depth of field, observing 3D microscopic objects using an optical microscope is an intrinsic problem.

As shown in Figure 6 b, the silhouette image of the pillars has a high contrast but the focus is blurry because of the limited depth of field. To overcome these problems, we could use an image fusion technique for a sequence of images taken by changing the position of the focus along the optical axis. This technique would provide sharp silhouettes even under a high magnification [ 19 ].

We demonstrated a simple and low-cost 3D shape acquisition method for transparent 3D printed microscopic objects. This method employed highly UV-absorbent 3D printed polymer objects to obtain high-contrast silhouette images of transparent 3D objects using UV transmitted illumination. Multiple silhouette images taken from different viewpoints made it possible to reconstruct the 3D shapes of the transparent 3D printed objects using the SFS method, with a 3D shape acquisition system constructed using a UV LED, a CCD camera and a rotation stage.

By changing the imaging lens, this system could be applicable to macro- and micro-scale models. In addition, transparent 3D printed models made from glass as well as polymer [ 20 , 21 ] could be observed using this method. Therefore, this method could be an inexpensive and useful tool for a 3D scanner and a way to inspect the appearance of transparent 3D objects without the need for time-consuming pre- and post-processing techniques. We thank Takashi Maekawa for the useful discussion on 3D shape reconstruction and for providing software for the SFS method.

National Center for Biotechnology Information , U. Journal List Micromachines Basel v. Micromachines Basel. Published online May Find articles by Keishi Koyama. Find articles by Masayuki Takakura.

Author information Article notes Copyright and License information Disclaimer. Received May 11; Accepted May This article has been cited by other articles in PMC. Abstract We propose and demonstrate a simple, low-cost, three-dimensional 3D shape acquisition method for transparent 3D printed microscopic objects.

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Keywords: 3D shape reconstruction, shape from silhouette, 3D printing, additive manufacturing, micro-stereolithography, transparent object, photopolymer. Introduction In recent years, various kinds of 3D printing technologies, from macro- to micro-scale devices, have been developed and widely used with a wide variety of materials including polymers, metals and ceramics [ 1 , 2 , 3 ].

Materials and Methods 2. Open in a separate window. Figure 1. Figure 2. Micro-Stereolithography Systems and Photocurable Resins In our experiments, two types of laboratory-made micro-stereolithography systems were used to make 3D printed micro-parts. Results and Discussion 3. Figure 3. Figure 4. Figure 5. Evaluating the Accuracy of 3D Shape Acquisition Using the Pillar Array Model To evaluate the accuracy of the 3D shape acquisition system, a pillar array model Figure 6 a containing pillars of different diameters was fabricated using micro-stereolithography based on the top-down system.

Figure 6. Table 1 Diameter of each pillar of a 3D printed pillar array model. Conclusions We demonstrated a simple and low-cost 3D shape acquisition method for transparent 3D printed microscopic objects. Acknowledgments We thank Takashi Maekawa for the useful discussion on 3D shape reconstruction and for providing software for the SFS method. Author Contributions K. Conflicts of Interest The authors declare no conflict of interest. References 1. Ligon S.

Polymers for 3D printing and customized additive manufacturing. William E. Metal additive manufacturing: A review. Deckers J. Additive manufacturing of ceramics: A review. Thompson A. X-ray computed tomography for additive manufacturing: A review. Saha S. Radiopaque resists for two-photon lithography to enable submicron 3D imaging of polymer parts via X-ray computed tomography.

ACS Appl. Chen F. Overview of three-dimensional shape measurement using optical methods. Moons T. Zhang R. Shape-from-shading: A survey. IEEE Trans.

Pattern Anal. Cheung G. Shape-from-silhouette across time part I: Theory and algorithms. Atsushi K. System for reconstruction of three-dimensional micro objects from multiple photographic images. Aided Des. Monri K. Three-dimensional ceramic molding based on microstereolithography for the production of piezoelectric energy harvesters. Actuators A. Maruo S.

Distance (Angles+Triangulation) - OpenCV and Python3 - Targeting Part 5

Recent progress in multiphoton microfabrication. Laser Photon. Gonen E. Scanning from heating: 3D shape estimation of transparent objects from local surface heating. Miyazaki D. Determining surface orientations of transparent objects based on polarization degrees in visible and infrared wavelengths. Marching cubes: A high resolution 3D surface construction algorithm.

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