| Solid modeling plays a key role in a variety of design and manufacturing activities. They serve as the foundation for applications like virtual sculpting, microstructure design and rapid prototyping, which usually deal with complex shape and topology model. The fast growing complexity of industrial models and the need to perform some operations repeatedly thereby necessitate an efficient processing system. However, since many fundamental geometric operations are compute-intensive, most commercial geometric kernels (e.g. Parasolid and ACIS), especially for freeform and highly-detailed complex models, require very high computational cost and do not work on such complex models. The purpose of this research is to exploit a solid modeler for freeform objects completely running on GPUs, which will greatly improve the efficiency of shape modeling.;A critical reason for such high computing intensity in operations is because they are performed on Boundary Representation (B-Rep). A new representation—named Layered Depth Normal Images (LDNI) is introduced in this thesis to describe an object directly on GPU. LDNI is an extension of ray-representation which inherits the good properties of Boolean simplicity, localization, domain decoupling and the ease of parallel implementation, therefore provides a more feasible form than usual boundary representations.;Performing fundamental operations (such as Boolean operations, Minkowski sum, etc) are often time-consuming and prone to numerical problems when being applied on B-Rep model. This work develops several GPU-accelerated algorithms, including Boolean and general Minkowski sum. Our algorithms are carried out on image-based representation and thus are able to perform much faster than conventional approaches without arise any significant instability or robustness issues.;Algorithms have also been developed for converting B-Rep to LDNI and vice versa on GPU. Our sampling method makes use of rasterization to get the images for high efficiency and GPGPU libraries to convert them back to B-Rep. With these algorithms, the representations of models can be converted between this framework and other common CAD kernels. The framework also supports local refinement of coarse mesh, which is useful for reducing memory consumption while maintaining G1 continuity.;Finally, the objective of this research is to provide a comprehensive framework for complex shape modeling problem. Several applications are demonstrated in using the GPU-based solid modeler, which shows great improvement in the efficiency compared with existing B-rep based systems. |
