From A Medial Surface To A Mesh

International audienceGiven a shape, a skeleton is a thin centered structure which jointly describes the topology and the geometry of the shape. Skeletons provide an alternative to classical boundary or volumetric representations, which is especially effective for applications where one needs to reason about, and manipulate, the structure of a shape. These skeleton properties make them powerful tools for many types of shape analysis and processing tasks. For a given shape, several skeleton types can be defined, each having its own properties, advantages, and drawbacks. Similarly, a large number of methods exist to compute a given skeleton type, each having its own requirements, advantages, and limitations. While using skeletons for two-dimensional (2D) shapes is a relatively well covered area, developments in the skeletonization of three-dimensional (3D) shapes make these tasks challenging for both researchers and practitioners. This survey presents an overview of 3D shape skeletonization. We start by presenting the definition and properties of various types of 3D skeletons. We propose a taxonomy of 3D skeletons which allows us to further analyze and compare them with respect to their properties. We next overview methods and techniques used to compute all described 3D skeleton types, and discuss their assumptions, advantages, and limitations. Finally, we describe several applications of 3D skeletons, which illustrate their added value for different shape analysis and processing tasks

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“…In theory, this allows reconstructing the exact O from its MAT(O), a process also known as garbing [DRF12,ASS11,JKT13]. 2).…”

Section: Reconstructibilitymentioning

confidence: 99%

“…2). In theory, this allows reconstructing the exact 풪 from its MAT( 풪 ), a process also known as garbing [DRF12, ASS11, JKT13]. Yet, practical skeletons approximate formal ones, due to approximate representations, sampling limits, or regularization.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

3D Skeletons: A State‐of‐the‐Art Report

Tagliasacchi

Delame

Spagnuolo

et al. 2016

Computer Graphics Forum

214

152

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“…Some of them are based on the implicit representation of the shape, from the skeleton [21,26]. Other methods [10,15,8] reconstruct the mesh from a discrete skeleton. Here, we describe skeletons in a continuous way, as canal surfaces.…”

Section: Related Work Skeletonmentioning

confidence: 99%

Towards Skeleton Based Reconstruction: From Projective Skeletonization to Canal Surface Estimation

Durix

Morin

Chambon

et al. 2015

2015 International Conference on 3D Vision

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We present a novel approach to reconstruct a 3D object from images corresponding to two different viewpoints: we estimate the skeleton of the object instead of its surface. The originality of the method is to be able to reconstruct a complete tubular 3D object from only two input images. Unlike classical reconstruction methods like multiview stereo, this approach does not rely on interest points but estimates the topology of the object and derives its surface. Our contributions are twofold. First, given two perspective images of the 3D shape, the projection of the skeleton is computed in 2D. Second the 3D skeleton is reconstructed from the two projections using triangulation and matching. A mesh is finally derived for each skeleton branch.

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“…This way, we measure the possible loss of geometric data in the cleaned skeleton relatively to the input skeleton. If we had used a garbing algorithm that consider other primitives to enhance the garbing mesh, like [12] that removes surface noise in the garbing mesh, the loss of geometric data would have been hidden. We chose the Root Mean Square distance (RMS), and we computed the distance between the input shape and the skin surface of each skeleton by the Mesh software [3].…”

Section: Protocolmentioning

confidence: 99%

Identifying and Structuring Skeletal Noise

Delame

Roudet

Faudot

2013

Lecture Notes in Computer Science

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Abstract. A skeleton is a thin centered structure within an object, which describes its topology and its geometry. The medial surface is one of the most known and used skeleton formulation. As other formulations, it contains noise, which complexifies its structure with useless parts. The connectivity of a skeleton is then unpredictable due to these useless parts. It can be a problem to segment the skeleton into logical components for example. We present here a technique whose purpose is to identify and structure such skeletal noise. It only requires a skeleton as input, making this work independent from any skeletonization process used to obtain the skeleton. We show in this paper that we significantly reduce the skeletal noise and produce clean skeletons that still capture every aspects of a shape. Those clean skeletons have the same local topology as the input ones, but with a clearer connectivity.

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From A Medial Surface To A Mesh

Cited by 6 publications

References 23 publications

3D Skeletons: A State‐of‐the‐Art Report

3D Skeletons: A State‐of‐the‐Art Report

Towards Skeleton Based Reconstruction: From Projective Skeletonization to Canal Surface Estimation

Identifying and Structuring Skeletal Noise

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