Consistent mesh partitioning and skeletonisation using the shape diameter function

Shapira, Lior; Shamir, Ariel; Cohen–Or, Daniel

doi:10.1007/s00371-007-0197-5

Cited by 496 publications

(437 citation statements)

References 24 publications

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“…The first camp aims to partition a solid object into volumetric components. They utilize part-aware shape descriptors such as concavity of the cuts [13,14], convexity [15,16] of parts, compactness [17] of parts, a shape diameter function [18], or a combination of these [19,20]. More sophisticated descriptors can be learned from a collection of shapes [21].…”

Section: Surface Segmentationmentioning

confidence: 99%

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%

Feature-aligned segmentation using correlation clustering

et al. 2017

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“…3), that allows for different refinement directions in different parts of the same object (refer to (Chen et al, 2009) for more details about these metrics). The compared method were Randomized and Normalized Cuts (Golovinskiy and Funkhouser, 2008), Shape Diameter Functions (Shapira et al, 2008), Core Extraction (Katz et al, 2005), Random Walks (Lai et al, 2008), Fitting Primitives (Attene et al, 2006a) and K-Means (Shlafman et al, 2002).…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…This is done by centering on each vertex spheres of increasing diameter and using the curves resulting by their intersection with the mesh as a characterizing descriptor for the clustering process. Shapira et al (2008) describe a method that exploits the Shape Diameter Function (SDF), a measure related to the object volume in the neighborhood of each point that is computed for the barycenter of each triangle. The segmentation procedure relies on a two phase process.…”

Section: Introductionmentioning

confidence: 99%

A graph-based technique for semi-supervised segmentation of 3D surfaces

Bergamasco

Albarelli

Torsello

2012

Pattern Recognition Letters

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a b s t r a c tA wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals, but rather, they are mostly designed to allow inexperienced users to acquire surfaces and whole objects easily and independently. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. In this paper we address the task of segmenting the acquired surfaces into perceptually relevant parts. Such a problem is well known to be ill-defined both for 2D images and 3D objects, as even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reason recent years have seen a great research effort into semi-supervised approaches, that can make use of small bits of information provided by the user to attain better accuracy. We propose a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by propagating part labels over a weighted graph representation of the surface directly derived from its triangulated mesh. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on a standard database of scanned 3D objects show its effectiveness even with moderate user supervision.

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“…3), that allows for different refinement directions in different parts of the same object (refer to [21] for more details about these metrics). The compared method were Randomized and Normalized Cuts [19], Shape Diameter Functions [16], Core Extraction [14], Random Walks [18], Fitting Primitives [17] and KMeans [11]. While most of these method are not supervised, some required parameters such as the number of segments to extract or initial seeds.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…This is done by centering on each vertex spheres of increasing diameter and using the curves resulting by their intersection with the mesh as a characterizing descriptor for the clustering process. Shapira et al [16] describe a method that exploits the Shape Diameter Function (SDF), a measure related to the object volume in the neighborhood of each point that is computed for the barycenter of each triangle. The segmentation procedure relies on a two phase process.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Segmentation of 3D Surfaces Using a Weighted Graph Representation

Bergamasco

Albarelli

Torsello

2011

Graph-Based Representations in Pattern Recognition

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Abstract. A wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals. By converse, they are mostly designed to allow inexperienced users to easily and independently acquire surfaces and whole objects. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. Specifically, in this paper we concentrate on the segmentation task applied to the acquired surfaces. Such a problem is well known to be ill-defined both for 2D images and 3D objects. In fact, even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reasons, we refrain from any attempt to offer an automatic solution. Instead we introduce a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by iteratively visiting a weighted graph representation of the surface starting from the supplied seeds. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on scanned 3D objects show its effectiveness and easiness of use.

show abstract

Consistent mesh partitioning and skeletonisation using the shape diameter function

Cited by 496 publications

References 24 publications

Feature-aligned segmentation using correlation clustering

Feature-aligned segmentation using correlation clustering

A graph-based technique for semi-supervised segmentation of 3D surfaces

Semi-supervised Segmentation of 3D Surfaces Using a Weighted Graph Representation

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