Good Random Multi-Triangulation of Surfaces

Medeiros, Esdras; Siqueira, Marcelo

doi:10.1109/tvcg.2017.2704078

Cited by 2 publications

(6 citation statements)

References 34 publications

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“…High accuracy, theoretical guarantee, and topology preservation are the advantages of HPDS-MT. Its results [36] show a minimal angle of > 30 o and maximal angle of < 120 o . The mesh's geometry is preserved by approximating the distribution of the Poisson disk radii over the surface.…”

Section: Remeshing With Optimizationmentioning

confidence: 94%

“…4A t , where E is the longest edge and A t is the area of the triangle [36]. In some articles, it is simply calculated as the ratio of the longest to the shortest edge length of a triangle [37].…”

Section: Triangle Qualitymentioning

confidence: 99%

“…One possible future direction is to fully automate this algorithm [124]. Hierarchical Poisson Disk sampling multi-triangulation (HPDS-MT) [36] combines HPDS and MT. The key objective is to generate a high-quality adaptive mesh.…”

Section: Remeshing With Optimizationmentioning

confidence: 99%

“…The mesh's geometry is preserved by approximating the distribution of the Poisson disk radii over the surface. This method [36] works for meshes with an empty boundary, and representation of a surface with a boundary is a future work. ODT [181] minimizes the interpolation error of DT with the same number of vertices.…”

Section: Remeshing With Optimizationmentioning

confidence: 99%

“…[1], [3], [5], [11], [12], [30], [32], [33], [44], [60], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83] θmin 45 [3], [5], [11], [12], [30], [33], [36], [54], [56], [59], [60], [65], [66], [67], [68], [70], [71], [72], [80], [84], [85], [86], [87], [88], [89],…”

mentioning

confidence: 99%

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Surface Remeshing: A Systematic Literature Review of Methods and Research Directions

Khan

Plopski

Fujimoto

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Triangle meshes are used in many important shape-related applications including geometric modeling, animation production, system simulation, and visualization. However, these meshes are typically generated in raw form with several defects and poor-quality elements, obstructing them from practical application. Over the past decades, different surface remeshing techniques have been presented to improve these poor-quality meshes prior to the downstream utilization. A typical surface remeshing algorithm converts an input mesh into a higher quality mesh with consideration of given quality requirements as well as an acceptable approximation to the input mesh. In recent years, surface remeshing has gained significant attention from researchers and engineers, and several remeshing algorithms have been proposed. However, there has been no survey article on remeshing methods in general with a defined search strategy and article selection mechanism covering the recent approaches in surface remeshing domain with a good connection to classical approaches. In this article, we present a survey on surface remeshing techniques, classifying all collected articles in different categories and analyzing specific methods with their advantages, disadvantages, and possible future improvements. Following the systematic literature review methodology, we define step-by-step guidelines throughout the review process, including search strategy, literature inclusion/exclusion criteria, article quality assessment, and data extraction. With the aim of literature collection and classification based on data extraction, we summarized collected articles, considering the key remeshing objectives, the way the mesh quality is defined and improved, and the way their techniques are compared with other previous methods. Remeshing objectives are described by angle range control, feature preservation, error control, valence optimization, and remeshing compatibility. The metrics used in the literature for the evaluation of surface remeshing algorithms are discussed. Meshing techniques are compared with other related methods via a comprehensive table with indices of the method name, the remeshing challenge met and solved, the category the method belongs to, and the year of publication. We expect this survey to be a practical reference for surface remeshing in terms of literature classification, method analysis, and future prospects.

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Section: Remeshing With Optimizationmentioning

confidence: 94%

Section: Triangle Qualitymentioning

confidence: 99%

Section: Remeshing With Optimizationmentioning

confidence: 99%

Section: Remeshing With Optimizationmentioning

confidence: 99%