GPU-Accelerated Progressive Boolean Operations on Polygonal Models

Boolean operations on polygonal models involve the complex intersection calculations and polygonal reconstruction, where the precision control and processing efficiency are two key problems. To reduce the Boolean operation complexity, this paper proposes a progressive and GPU accelerated Boolean operation approach to generate levels-of-detail polygonal models. Layered depth images are employed to approximate the enclosed boundaries of polygons and the intersection calculations are performed as the in/out classification of axis-aligned sampling points. To avoid the additional sampling process for levels-of-detail models, the boundary points are progressively merged into low-resolution cubes. The feature-preserving dual contouring algorithm is adopted to convert boundary points into a mesh model. The proposed algorithm can be implementation in parallel on GPU with the hardware-supported CUDA. Finally, experimental results show the feasibility of the proposed approach.