Heat-mapping: A robust approach toward perceptually consistent mesh segmentation

Fang, Yi; Sun, Mengtian; Kim, Minhyong; Ramani, Karthik

doi:10.1109/cvpr.2011.5995695

Cited by 44 publications

(32 citation statements)

References 27 publications

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“…In this study, we use heat mean signature (HMS) (Fang et al 2011) to evaluate the weight of a point. The bigger is the value of HMS, the more influential of a point is.…”

Section: Highlight Detailsmentioning

confidence: 99%

A novel framework for 3D shape retrieval

Yao

Zhang

et al. 2017

Appl Inform

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“…In this study, we use heat mean signature (HMS) (Fang et al 2011) to evaluate the weight of a point. The bigger is the value of HMS, the more influential of a point is.…”

Section: Highlight Detailsmentioning

confidence: 99%

A novel framework for 3D shape retrieval

Yao

Zhang

et al. 2017

Appl Inform

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“…Subsequently, the defined function is used by a grouping algorithm which provides a segmentation. For instance, the Heat Mapping approach [8] defines a vertex signature which can be interpreted as the average temperature on the surface by applying heat on a vertex. Then, a segmentation process using the k-means algorithm is driven from points with the highest value of heat affinity.…”

Section: Related Workmentioning

confidence: 99%

Key-components: detection of salient regions on 3D meshes

Sipiran

Bustos

2013

Vis Comput

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In this paper, we present a method to detect stable components on 3D meshes. A component is a salient region on the mesh which contains discriminative local features. Our goal is to represent a 3D mesh with a set of regions, which we called key-components, that characterize the represented object and therefore, they could be used for effective matching and recognition. As key-components are features in coarse scales, they are less sensitive to mesh deformations such as noise. In addition, the number of keycomponents is low compared to other local representations such as keypoints, allowing us to use them in efficient subsequent tasks. A desirable characteristic of a decomposition is that the components should be repeatable regardless shape transformations. We show in the experiments that the keycomponents are repeatable and robust under several transformations using the SHREC'2010 feature detection benchmark. In addition, we discover the connection between the theory of saliency of visual parts from the cognitive science and the results obtained with our technique.

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“…The algorithms based on diffusion metrics [16,6,12,15,8] are promising as they explore the intrinsic structure of the 3D mesh by exploiting the rich yet implicit information encoded by diffusion kernel. The authors in [16] develop a deformation invariant shape signature, namely the GP S, based on eigenvalues and eigenfunctions of the Laplace-Beltrami operator (denoted as the Laplacian henceforth).…”

Section: Related Workmentioning

confidence: 99%

Center-Shift: An approach towards automatic robust mesh segmentation (ARMS)

Sun

Fang

Ramani

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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In the area of 3D shape analysis, research in mesh segmentation has always been an important topic, as it is a fundamental low-level task which can be utilized in many applications including computer-aided design, computer animation, biomedical applications and many other fields. We define the automatic robust mesh segmentation (ARMS) method in this paper, which 1) is invariant to isometric transformation, 2) is insensitive to noise and deformation, 3) performs closely to human perception, 4) is efficient in computation, and 5) is minimally dependent on prior knowledge. In this work, we develop a new framework, namely the Center-Shift, which discovers meaningful segments of a 3D object by exploring the intrinsic geometric structure encoded in the biharmonic kernel. Our Center-Shift framework has three main steps: First, we construct a feature space where every vertex on the mesh surface is associated with the corresponding biharmonic kernel density function value. Second, we apply the Center-Shift algorithm for initial segmentation. Third, the initial segmentation result is refined through an efficient iterative process which leads to visually salient segmentation of the shape. The performance of this segmentation method is demonstrated through extensive experiments on various sets of 3D shapes and different types of noise and deformation. The experimental results of 3D shape segmentation have shown better performance of Center-Shift, compared to state-of-the-art segmentation methods.

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Heat-mapping: A robust approach toward perceptually consistent mesh segmentation

Abstract: 3D mesh segmentation is a fundamental low-level task with applications in areas as diverse as computer vision

Cited by 44 publications

References 27 publications

A novel framework for 3D shape retrieval

A novel framework for 3D shape retrieval

Key-components: detection of salient regions on 3D meshes

Center-Shift: An approach towards automatic robust mesh segmentation (ARMS)

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