B-rep model simplification using selective and iterative volume decomposition to obtain finer multi-resolution models. (July 2019)

• Record Type: Journal Article
• Authors: Kwon, Soonjo; Mun, Duhwan; Kim, Byung Chul; Han, Soonhung; Suh, Hyo-Won
• Is Part Of: Computer aided design. Volume 112(2019)
• Page Start: 23
• Page End: 34
• Publication Date: 2019-07
• Subjects: B-rep model -- Selective and iterative volume decomposition -- Feature recognition -- Feature-based simplification -- Level of detail -- Multi-resolution; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2019.03.003
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: A model simplification technique is required to control the level of detail (LOD) of a three-dimensional (3D) computer-aided design (CAD) model. In particular, to simplify a B-rep model, volume decomposition operations are performed to decompose the model into volumes, and the decomposed volumes are then sequentially removed according to their importance. Volume decomposition operations can be categorized into wrapping and cutting decompositions, depending on the type of the acquired delta volume. If both wrapping and cutting decompositions can be applied to the same region, a criterion should be established for selecting the appropriate operation for that region. In this paper, we propose selective and iterative volume decomposition that combines four wrapping and two cutting decomposition operations. In addition, we propose volume and area ratios as the selection criteria for wrapping and cutting. We conducted experiments involving two part models and two assembly models to validate the proposed method. The experimental results showed that the proposed method produces a larger number of decomposed volumes with various volume distributions compared to previous methods. In summary, the proposed method is a highly effective approach for achieving incremental model simplification and obtaining multi-resolution models. Highlights: Selective and iterative volume decomposition is proposed. The proposed method combines four wrapping and two cutting decomposition operations. Volume ratio and the area ratio are proposed as criteria for operation selection. Experiments are performed to validate the proposed method. Incremental model simplification and multi-resolution models are obtained.