Reconstruction using a simple triangle removal approach

Methirumangalath, Subhasree; Parakkat, Amal Dev; Kannan, Shyam Sundar; Muthuganapathy, Ramanathan

doi:10.1145/3145749.3149447

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…Insertion algorithms insert new points into a DT while keeping the Delaunay criterion [148]. Triangle removal is another strategy [106] for constructing DT while handling noise and sharp features. Sink-insertion [122] can be used to improve efficiency while maintaining the mesh quality of DT.…”

Section: Thementioning

confidence: 99%

“…3.5 S34 [54] Fast being fewer Steiner points, high quality and size optimality Only for 2D domain 3.0 S35 [85] Efficient being avoiding very skinny DT computation Only for 2D domain 2.5 S36 [10] Efficient, robust and handle degeneracy Losses efficiency for some data 2.5 S37 [9] More efficient than sequential SVR No quality consideration 2.0 S38 [68] Efficient, robust, can handle large models without partitioning Maximal angle and sharp features are not considered 2.5 S39 [72] First theoretical analysis of MPS for surface and quality remeshing Fails in sharp edges and complex input 4.0 S40 [119] Real-time visualization with sharp features Low quality in sharp edges 2.5 S41 [149] Efficient, with consideration of connectivity, regularity and density Fails in complex surface meshes 3.5 S42 [106] Sharp feature preservation Parametric algorithm, based on trial and error 2.0 S43 [107] Better feature preservation and Efficient Lack of other quality metrics 3.5 S44 [92] Noise removal, and quality improvement with sharp feature preservations Fails to improve angle quality in sharp features 3.5 S45 [90] Robust, efficient, and uses fewer Steiner points to improve minimal angle Fails with holes and limited to 2D only 2.0 S46 [91] Simple, robust, works efficiently in real-time Fails with holes and limited to 2D only 3.5 S47 [71] Efficient, optimal memory usage, quality improvement Fails with noisy inputs 4.0 S48 [115] Improved efficiency with bypassing the curve reconstruction Fails in curves with singularities, limited to 2D 3.0 S49 [122] Simple parallelization, efficient…”

Section: S08 [48] Geometric Approximationmentioning

confidence: 99%

“…Furthermore, it also fails for noisy and complex models. A triangle removal algorithm [106] can be used to preserve sharp features during surface reconstruction. However, it is a parametric algorithm, requiring a trial and error mechanism for its determination.…”

Section: S08 [48] Geometric Approximationmentioning

confidence: 99%

“…[9], [40], [74], [85], [115], [122], [123], [128] Sharp features 16 [11], [33], [45], [52], [74], [75], [92], [96], [100], [102], [106], [107], [112], [116], [119], [125] Genus 7 [1], [39], [61], [63], [66], [99], [ methodology, or same specific domain. We classified the selected articles into 9 different categories based on their remeshing methodologies.…”

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: Thementioning

confidence: 99%

Section: S08 [48] Geometric Approximationmentioning

confidence: 99%

Section: S08 [48] Geometric Approximationmentioning

confidence: 99%

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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“…In the triangulation algorithm, the Delaunay triangulation algorithm can obtain the optimal triangulation grid. According to the different implementation processes, Delaunay triangulation can be divided into point-by-point insertion method [36], region growth method, divide-and-conquer method [37], and random Delaunay filtering method [38]. In this paper, the Delaunay triangulation algorithm based on region growth is adopted.…”

Section: Greedy Projection Triangulationmentioning

confidence: 99%

Geometric Modeling of Rosa roxburghii Fruit Based on Three-Dimensional Point Cloud Reconstruction

Xie

Lang

Chen

2021

Journal of Food Quality

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Fruit three-dimensional (3D) model is crucial to estimating its geometrical and mechanical properties and improving the level of fruit mechanical processing. Considering the complex geometrical features and the required model accuracy, this paper proposed a 3D point cloud reconstruction method for the Rosa roxburghii fruit based on a three-dimensional laser scanner, including 3D point cloud generation, point cloud registration, fruit thorns segmentation, and 3D reconstruction. The 3D laser scanner was used to obtain the original 3D point cloud data of the Rosa roxburghii fruit, and then the fruit thorns data were removed by the segmentation algorithm combining the statistical outlier removal and radius outlier removal. By analyzing the effects of five-point cloud simplification methods, the optimal simplification method was determined. The Poisson reconstruction algorithm, the screened Poisson reconstruction algorithm, the greedy projection triangulation algorithm, and the Delaunay triangulation algorithm were utilized to reconstruct the fruit model. The number of model vertices, the number of facets, and the relative volume error were used to determine the best reconstruction algorithm. The results indicated that this model can better reconstruct the actual surface of Rosa roxburghii fruit. The method provides a reference for the related application.

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