Fast and robust detection of crest lines on meshes

Yoshizawa, Shin; Belyaev, Alexander; Seidel, Hans‐Peter

doi:10.1145/1060244.1060270

Cited by 144 publications

(135 citation statements)

References 24 publications

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“…As mentioned earlier, we use Ref. [51] to obtain the ridge and valley lines as input to our method. To compute CC on the watershed oversegmentation, we employ the lifted multi-cut method (LMP) [10] which is currently the fastest serial solver on large sparse graphs.…”

Section: Resultsmentioning

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: Resultsmentioning

confidence: 99%

“…We use the method of Yoshizawa et al [51] due to its robustness. Like many other feature line algorithms, each feature line produced by Yoshizawa et al's method is represented by feature points lying on triangle edges that are connected by feature segments inside triangles.…”

Section: Overviewmentioning

confidence: 99%

Feature-aligned segmentation using correlation clustering

et al. 2017

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“…In the context of complex geology-driven applications, besides the problem of correctly recovering surface topology, sharp feature lines like ridges/ravines or crest lines [17], [27], [25] need to be identified. Concerning point sets, there have been several efforts for robust feature detection [15][5] [8].…”

Section: Related Workmentioning

confidence: 99%

Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets

Keller¹,

Kreylos²,

Vanco³

et al. 2010

Mathematics and Visualization

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Abstract. We present a user-assisted approach to extracting and visualizing structural features from point clouds obtained by terrestrial and airborne laser scanning devices. We apply a multi-scale approach to express the membership of local point environments to corresponding geometric shape classes in terms of probability. This information is filtered and combined to establish feature graphs which can be visualized in combination with the color-encoded feature and structural probability estimates of the measured raw point data. Our method can be used, for example, for exploring geological point data scanned from multiple viewpoints.

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“…The first two types of vertices are fixed during the smoothing process for feature preservation and the last two are referred to as adjustable nodes. More sophisticated algorithms for detecting salient features such as crest lines on discrete surfaces can be adopted [8]. Then in an iterative manner, a small optimal displacement is computed for each adjustable node using the ILSA, which accounts for some geometric factors.…”

Section: Outline Of the Smoothing Proceduresmentioning

confidence: 99%

An Improved Laplacian Smoothing Approach for Surface Meshes

Chen

Zheng

Chen

et al. 2007

Computational Science – ICCS 2007

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Abstract. This paper presents an improved Laplacian smoothing approach (ILSA) to optimize surface meshes while maintaining the essential characteristics of the discrete surfaces. The approach first detects feature nodes of a mesh using a simple method, and then moves its adjustable or free node to a new position, which is found by first computing an optimal displacement of the node and then projecting it back to the original discrete surface. The optimal displacement is initially computed by the ILSA, and then adjusted iteratively by solving a constrained optimization problem with a quadratic penalty approach in order to avoid inverted elements. Several examples are presented to illustrate its capability of improving the quality of triangular surface meshes.

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Fast and robust detection of crest lines on meshes

Cited by 144 publications

References 24 publications

Feature-aligned segmentation using correlation clustering

Feature-aligned segmentation using correlation clustering

Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets

An Improved Laplacian Smoothing Approach for Surface Meshes

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