Semantic-based parametric control of CAD model conversion for large model visualization in virtual reality

CAD systems offer typically some visualization tools to have a better understanding of the model. Although these possibilities have evolved considerably, even providing 3D interactive inspection of the models in Virtual Reality (VR), many CAD systems still lack of good performance in this matter, especially when Large Models are considered. This kind of conversion from CAD format to VR typically considers two factors: the modell (size, structure, complexity...) and the resources (memory, processor, graphics...). Thus, a key factor is usually forgotten: the user. User knowledge can bring semantics to the process, and allow a better Large Model Visualization (LMV) for this purpose. In this paper we explain how semantics can help in the CAD conversion for LMV, and improve the performance of visualization in environments with restricted resources. We propose a semantic-based parametric control of the conversion process to manage how different visualization techniques are applied for a specific model.