Patch layout generation by detecting feature networks

Cao, Yuanhao; Yan, Dong‐Ming; Wonka, Peter

doi:10.1016/j.cag.2014.09.022

Cited by 18 publications

(27 citation statements)

References 42 publications

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“…Existing explicit methods [7,8,36] start by pruning away weak feature lines. Afterwards, Lévy et al [36] as well as Mitani and Suzuki [7] consider the watershed of the distance function from the pruned feature lines and merge small patches, while Cao et al [8] directly extend and connect pruned feature lines. Both methods are based on heuristics that are controlled by several thresholds.…”

Section: Surface Segmentationmentioning

confidence: 99%

“…Also, note that the curve networks produced by the feature extension method of Ref. [8] (without the subsequent stage of refinement) are not guaranteed to yield a segmentation of the surface. For example, a closed curve on a high-genus surface does not necessarily partition the surface into two patches.…”

Section: Surface Segmentationmentioning

confidence: 99%

“…Compared to implicit methods, our method specifically addresses the goal of including prominent feature lines while avoiding weak feature lines or non-feature lines. In contrast to existing explicit methods [7,8,36], our method rests upon a clear mathematical formulation (correlation clustering). The formulation allows us to perform both feature pruning and connection in a single step, thereby reducing the number of parameters to be tuned (our method has a single parameter α that controls the edge weights).…”

Section: Surface Segmentationmentioning

confidence: 99%

“…Unlike previous segmentation methods that also utilize feature lines [7,8,36], our method requires no pruning of features. Instead, selection and connection of features are handled simultaneously in the CC formulation.…”

Section: Sensitivity To Inputmentioning

confidence: 99%

“…1(b) and 1(c). Only a few methods explicitly aim to preserve a given set of feature lines as the patch boundaries [7,8]. However, they all rely on heuristics that involve multiple parameters.…”

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: Surface Segmentationmentioning

confidence: 99%

Section: Surface Segmentationmentioning

confidence: 99%

Section: Surface Segmentationmentioning

confidence: 99%

Section: Sensitivity To Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-aligned segmentation using correlation clustering

et al. 2017

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Surface remeshing with robust user-guided segmentation

et al. 2018

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Surface remeshing is widely required in modeling, animation, simulation, and many other computer graphics applications. Improving the elements' quality is a challenging task in surface remeshing. Existing methods often fail to efficiently remove poor-quality elements especially in regions with sharp features. In this paper, we propose and use a robust segmentation method followed by remeshing the segmented mesh. Mesh segmentation is initiated using an existing Live-wire interaction approach and is further refined using local mesh operations. The refined segmented mesh is finally sent to the remeshing pipeline, in which each mesh segment is remeshed independently. An experimental study compares our mesh segmentation method as well as remeshing results with representative existing methods. We demonstrate that the proposed segmentation method is robust and suitable for remeshing.

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Simple primitive recognition via hierarchical face clustering

Yang

Jia

2020

Comp. Visual Media

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We present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.

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Patch layout generation by detecting feature networks

Cited by 18 publications

References 42 publications

Feature-aligned segmentation using correlation clustering

Feature-aligned segmentation using correlation clustering

Surface remeshing with robust user-guided segmentation

Simple primitive recognition via hierarchical face clustering

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