Abstract
The benefits of multi-resolution modeling techniques in virtual reality are vast, but one essential component of this model is how it can be used to speedup the process of virtual design and virtual prototyping. In this paper we propose a new multi-resolution representation scheme called MRM, which can support efficient extraction of both fixed and variable resolution modeling data for handling multiple objects in the same scene. One important feature of the MRM scheme is that it supports unified selective simplifications and selective refinements over the mesh representation of the object. In addition, multi-resolution models may be used to support real-time geometric transmission of data in collaborative virtual design and prototyping applications. These key features in MRM, may be applied to a variety of VR applications.
Highlights
MULTI-RESOLUTION MODELSOne traditional heuristic technique used to manipulate the multi-resolution model is to author several versions of another model at various levels of detail (LoD) [6,7] (Fig. 1 is an example)
The benefits of multi-resolution modeling techniques in virtual reality are vast, but one essential component of this model is how it can be used to speedup the process of virtual design and virtual prototyping
The levels of detail (LoD) and multi-resolution models are widely used in terrain visualization, flight simulation, topographic surfaces in Geographical Information Systems and general virtual environments [1,3,4,5,6], few works have been done on applying these techniques to virtual design and prototyping fields
Summary
One traditional heuristic technique used to manipulate the multi-resolution model is to author several versions of another model at various LoD [6,7] (Fig. 1 is an example). The multi-resolution representation is a data structure that allows the compact representation of a number m of representations, at different levels of detail, where m is somewhat proportional to the data size. With multi-resolution models, users have much more flexibility in the selection of the “best” level of detail according to run-time needs instead of the fixed models created during the preprocessing simplification phase
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