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2024 | OriginalPaper | Buchkapitel

A Shrinkwrap Method for Quickly Generating Virtual Prototypes for Extended Reality

verfasst von : Paolo Senesi, Barbara Lonzi, Alessandra Papetti, Michele Germani, Marco Mandolini

Erschienen in: Design Tools and Methods in Industrial Engineering III

Verlag: Springer Nature Switzerland

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Abstract

Extended Reality (XR) applications require Photorealistic Virtual Prototypes (PVPs), usually obtained by generating a polygon-based textured model of the original CAD (Computer Aided Design). Getting PVPs requires specific rendering and texturing software tools that experienced technicians use. Automatic simplification and conversion approaches from CAD to XR exist but are almost all focused on models without textures. The paper aims to establish a semi-automatic method for creating low-poly PVPs for XR starting from 3D CAD models. The process, implemented in Blender, consists of several steps. First, starting from a high-poly model imported from a 3D mechanical CAD system, the modelling cage (a low-poly simplified version) is designed. Second, a low-poly variety of the CAD geometries are generated using Blender modifiers (i.e., shrinkwrap, subdivision surface and Boolean). Texture mapping is carried out on the cage. Then, by combining Shrinkwrap, Boolean and Subdivision Surface modifiers, the cage is projected on the CAD-imported high-poly model. Thus, it becomes a low-poly version of the same geometry. Finally, the Normal Baking process adds high-frequency details (e.g., engravings). Thanks to the generation of a UV-Mapped cage wrapping up the component, in case of local modifications to the latter, Blender semi-automatically updates the PVP. The method was used on three stock versions of a sporting rifle. With an average duration of 23 min, the proposed approach more than halved the time required with manual modelling techniques.

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Literatur
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Metadaten
Titel
A Shrinkwrap Method for Quickly Generating Virtual Prototypes for Extended Reality
verfasst von
Paolo Senesi
Barbara Lonzi
Alessandra Papetti
Michele Germani
Marco Mandolini
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-58094-9_38

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