Variational mesh segmentation via quadric surface fitting

Yan, Dong‐Ming; Wang, Wenping; Liu, Yang; Yang, Zhouwang

doi:10.1016/j.cad.2012.04.005

Cited by 116 publications

(85 citation statements)

References 37 publications

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“…As observed earlier in Fig. 1, primitive-fitting [5] and region-growing [6] tend to create additional boundaries in featureless regions where there is large fitting error or variation in curvature. On the other hand, prominent feature lines lying in homogeneous regions may be ignored.…”

Section: Comparisons and Examplessupporting

confidence: 66%

“…Ideally, the feature lines emerge implicitly as the boundaries of the patches. The regularity condition can be low approximation error by an analytical primitive [5,[22][23][24][25], developability [26,27], or similarity using a variety of shape descriptors, including curvature [6,[28][29][30][31], normal voting tensor [32], slippage [33], and diffusion-type distances to a set of seed locations [34,35]. However, implicit methods can oversegment surface regions that are void of prominent feature lines, if the region fails the regularity conditions.…”

Section: Surface Segmentationmentioning

confidence: 99%

“…On the other hand, Mitani and Suzuki use a heuristic merging process with an area-based termination criterion. Even with our best efforts to tune this termination criterion, their [5], the region-growing method of Nieser et al [6], the feature-guided method of Mitani and Suzuki [7], and our method, on three examples. method produces much less satisfactory results: the segmentations tend to miss important, fine features and include many weak feature lines in the patch boundaries.…”

Section: Comparisons and Examplesmentioning

confidence: 99%

“…[5] for comparison. The models in this paper were obtained from AIM@SHAPE and Princeton Segmentation Benchmark.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…We show how feature-aligned mesh segmentation can be cast as a CC problem by defining an appropriate weighted graph on the Fig. 1 Comparisons of patch-based segmentation of (a) the Rockerarm created by (b) the primitive-fitting method of Yan et al [5], (c) the region-growing method of Nieser et al [6], (d) the curvaturesimilarity guided method of Mitani and Suzuki [7] obtained from the ridge (red) and valley (blue) lines in (a), and (e) our method (with α = 0.6), using the same set of feature lines. Note that existing methods often miss prominent features (see blue boxes) or produce too many segments in featureless regions (see red boxes).…”

Section: Introductionmentioning

confidence: 99%

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Feature-aligned segmentation using correlation clustering

et al. 2017

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We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.

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Section: Comparisons and Examplessupporting

confidence: 66%

Section: Surface Segmentationmentioning

confidence: 99%

Section: Comparisons and Examplesmentioning

confidence: 99%

“…[5] for comparison. The models in this paper were obtained from AIM@SHAPE and Princeton Segmentation Benchmark.…”

Section: Acknowledgementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-aligned segmentation using correlation clustering

et al. 2017

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Intelligent Systems for Volumetric Feature Recognition from CAD Mesh Models

Hase¹,

Bhalerao²,

Verma³

et al. 2019

EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

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This paper presents an intelligent technique to recognize the volumetric features from CAD mesh models based on hybrid mesh segmentation. The hybrid approach is an intelligent blending of facet-based, vertex based, rulebased, and machine learning based techniques. Comparing with existing stateof-the-art approaches, the proposed approach does not depend on attributes like curvature, minimum feature dimension, number of clusters, number of cutting planes, the orientation of model and thickness of the slice to extract volumetric features. The intelligent threshold prediction makes hybrid mesh segmentation automatic. The proposed technique automatically extracts volumetric features like blends and intersecting holes along with their geometric parameters. The proposed approach has been extensively tested on various benchmark test cases. The proposed approach outperforms the existing techniques favorably and found to be robust and consistent with coverage of more than 95% in addressing volumetric features

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Quantitative Performance Analysis of Hybrid Mesh Segmentation

Hase

Bhalerao

Nagarkar

et al. 2020

EAI/Springer Innovations in Communication and Computing

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This paper presents a comprehensive quantitative performance analysis of hybrid mesh segmentation algorithm. An important contribution of this proposed hybrid mesh segmentation algorithm is that it clusters facets using "facet area" as a novel mesh attribute. The method does not require to set any critical parameters for segmentation. The performance of the proposed algorithm is evaluated by comparing the proposed algorithm with the recently developed state-of-the-art algorithms in terms of coverage, time complexity, and accuracy.The experimentation results on various benchmark test cases demonstrate that Hybrid Mesh Segmentation approach does not depend on complex attributes, and outperforms the existing state-of-the-art algorithms. The simulation reveals that Hybrid Mesh Segmentation achieves a promising performance with coverage of more than 95%.

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Variational mesh segmentation via quadric surface fitting

Cited by 116 publications

References 37 publications

Feature-aligned segmentation using correlation clustering

Feature-aligned segmentation using correlation clustering

Intelligent Systems for Volumetric Feature Recognition from CAD Mesh Models

Quantitative Performance Analysis of Hybrid Mesh Segmentation

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