Development of a virtual reality simulator replacingthe check valve on christmas tree

The article describes the process of designing and creating a software environment that allows in automatic mode to create a realistic landscape. A review of existing approaches to landscape generation is carried out, which have a set of disadvantages taken into account when developing a software environment. A diagram of components and main classes is described. The developed subroutine that implements the polygon mesh generation algorithm provides an interface for creating and editing a mesh of hexagons on a plane, used for simplified work with biomes, as well as detailing the boundaries of polygons to give the landscape elements of randomness and, as a result, realism. The process uses the Diamond Square noise generation algorithm. The docking algorithm is designed to reduce the gaps between the heights of different biomes. The erosion algorithm uses particles generated on a height mapto carry soil particles in accordance with physical laws. The user interface of the application and the results of the algorithms are presented.

3D Polygon Mesh Encryption Based on 3D Lorenz Chaotic Map

<p class="0abstract">The multimedia application developments in recent years lead to the widespread of 3D model applications. It becomes more popular in various fields as well as exchanging it over the internet. The security of the 3D models is a very important issue now a day, so the scheme for encrypting the 3D model will be proposed in this work. In this proposed scheme, the 3D polygon mesh model will be protected through the encrypting process based on a 3D Lorenz Chaotic map where the vertices value of the 3D polygon mesh model will be modified using 3D keys generated by 3D Lorenz Chaotic Map, which has excellent property and provides a good diffusion. The proposed scheme was implemented on various 3D models, which have a different number of vertices and faces. The experimental results show that the proposed scheme has good encryption results, which were noted through completely deforming and changing the whole shape of the 3D models. The Hausdorff Distance (HD) and histogram metrics are adopted to calculate the matching degree between the original and extracted model. The results show that the original and extracted model are identical through the values of HD, where they are approximately zero, and the histogram visually is identical. </p>

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

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.

43 Digital Human Project: 3D Photogrammetry for Human Cadaveric Pelvic Specimens: An Innovation in Colorectal Anatomical Education

Introduction Cadaveric dissection remains an essential aspect of anatomical education but is not readily available to the majority of surgical trainees. 3D photogrammetry is the process of creating a 3D model from a series of 2D images and has tremendous potential in anatomical education. We describe a novel low-cost single-camera 3D photogrammetry technique to reconstruct cadaveric specimens as digital models. Method A formalin preserved hemipelvis was mounted on a turntable. Photos were taken sequentially at 5 o increments through 360° at three different fixed viewing angles (n = 216 photos) using a mirrorless camera with a 12-60mm f3.5-5.6 kit lens. Four surroundings LED standing lights were used to ensure diffuse ambient lighting of the specimen. Photos were imported into Agisoft Metashape software in order to generate a point cloud and produce the final virtual model composed of a polygon mesh. Results The specimen was successfully reconstructed and can be visualised at; https://sketchfab.com/3d-models/pelvic-sidewall-b76450b787824c968f864791d47318f2. The total processing time was 20 hrs. Conclusions Through this technique, we can produce accurate, interactive, and accessible 3D prosection models for surgical education. The method could be employed to establish a digital library of human anatomy for surgical training worldwide.

Fitting CAD data to scanned data with large deformation☆

Recently, largely deformed simulation models, such as car crash simulations, have been in high demand. To evaluate such models, it is necessary to use actual deformation results to compare and validate them. When measuring actual deformed objects, an X-ray CT is useful because it is non-destructive. However, matching undeformed CAD data and scanned deformation data is difficult. We propose a system for users to set control points or control lines on feature points and predict deformation using affine transformation with the moving least squares method. In the proposed method, undesirable distortions are reduced by evaluating scaling using singular value index and introducing offset control points. The deformed CAD data are obtained by matching predicted CAD data and a polygon mesh generated by deformed CT data. In addition, the surface elements of the generated deformed CAD data are evaluated for CAE.