Section: New Results
Keywords : Huge models, Mesh Analysis, Computational Results Analysis, Multiresolution algorithms, Out-of-core Data Structures, Virtual Prototyping, CAD.
Multi-resolution Analysis of Huge Digital Mock-ups in Virtual Reality
We work on the importation of huge digital mock-ups in Virtual Reality. This work is the subject of the PhD thesis of Jean-Marie Souffez supervised by Georges Dumont, and part of the RNTL SALOME 2 project. It is based on OpenMASK for the Virtual Reality applications, and SALOME platform for the production of digital mock-ups. The goal is to interactively handle these models in a VR scene, to allow their virtual prototyping.
The Product Development Process has taken benefit from advances in design, simulation and validation processes. Digital mock-ups have thus become too complex to be straightforwardly handled by graphics hardware, the associated mesh and computational results being usually huge and not fitting in-core.
In this context, the use of Virtual Reality as a tool for Virtual Prototyping can provide an easier analysis of meshes and of computational results, by ensuring interactive manipulation of the model. This, in particular, allows to test more parameters for the scientific computations, and ensures easier collaborative design.
As the digital mock-ups are too big to be straightforwardly handled by a single PC hardware, it is necessary to implement a level-of-detail (LOD) framework, that will control the size of the model at run-time.
The solution we implemented is a multi-resolution framework that provides easy out-of-core management of the whole model and ensures direct access to the original mock-up. It is based on a partition of the input mesh into several sub-meshes and on the dual graph of the partition. Several under-samplings are generated for each sub-mesh. Computational results can then be loaded on particular sub-meshes at run-time, allowing fast and easy analysis of the whole model, and allowing local analysis of the model at its original resolution.
In comparison to state-of-the-art algorithms, our method is based on a graph-partitioning algorithm (rather than space-subdivision algorithms). The graph partitioning algorithms allow to partition the model with regard to the attributes of the mesh (such as vertex colors, face normals, etc.). This permits to partition the input model with regards to the computational results that are associated to it.
The multi-resolution framework we propose handles both polygon-based and polyhedron-based models. Results of the pre-processing of a model is shown in Figure 2 . Screenshots from the interactive, out-of-core analysis of meshes are shown in Figures 3 and 4 .