Mesh saliency with global rarity

Wu, Jian‐Ying; Shen, Xiaoyong; Zhu, Wei; Liu, Ligang

doi:10.1016/j.gmod.2013.05.002

Cited by 77 publications

(66 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [12], Wu et al detect salient regions with a descriptor measuring the local height field into the neighborhood of each vertex; a square map of projection heights [13] is generated to denote its form. Local and global saliencies are computed for each vertex.…”

Section: State-of-the-artmentioning

confidence: 99%

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Nouri

Charrier

Lézoray

2015

Signal Processing: Image Communication

View full text Add to dashboard Cite

a b s t r a c tOur visual attention is attracted by specific areas into 3D objects (represented by meshes). This visual attention depends on the degree of saliency exposed by these areas. In this paper, we propose a novel multi-scale approach for detecting salient regions. To do so, we define a local surface descriptor based on patches of adaptive size and filled in with a local height field. The single-scale saliency of a vertex is defined as its degree measure in the mesh with edges weights computed from adaptive patch similarities weighted by the local curvature. Finally, the multi-scale saliency is defined as the average of single-scale saliencies weighted by their respective entropies. The contribution of the multi-scale aspect is analyzed and showed through the different results. The strength and the stability of our approach with respect to noise and simplification are also studied. Our approach is compared to the state-of-the-art and presents competitive results.

show abstract

Section: State-of-the-artmentioning

confidence: 99%

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Nouri

Charrier

Lézoray

2015

Signal Processing: Image Communication

View full text Add to dashboard Cite

show abstract

“…It also raised the issue that global properties such as segment centeredness and proximity to a symmetry axis are required to explain more subtle salient features. [35] proposed an approach for detecting mesh saliency based on the observation that salient features are both locally prominent and globally rare. [30] analysed the log-Laplacian spectrum of meshes and presented a method where both local geometric cues and global information corresponding to the lowfrequency end of the spectrum are considered.…”

Section: Related Workmentioning

confidence: 99%

Local-to-global mesh saliency

et al. 2016

View full text Add to dashboard Cite

As a measure of regional importance in agreement with human perception of 3D shape, mesh saliency should be based on local geometric information within a mesh but more than that. Recent research has shown that global consideration has a significant role in mesh saliency. This paper proposes a local-to-global framework for computing mesh saliency where we offer novel solutions to solve three inherent problems: (1) an algorithm based on statistic Laplacian which does not only compute local saliency, but also facilitates the later computation of global saliency; (2) a local-to-global method based on pooling and global distinctness to compute global saliency; (3) a framework to integrate local and global saliency. Experiments demonstrate that our approach can effectively detect salient features consistent with human perceptual interest. We also provide comparisons to existing state-of-the-art methods for mesh saliency and show improved results produced by our method.

show abstract

“…An adaptive inhibition process chooses only the vertices characterized by values larger than 85 % of their mean neighborhood values, and from these, only vertices that are local maxima and have a value higher than 30 % of the global maximum are retained as points of interest. In Wu et al (2013), the mesh saliency is computed considering both the local contrast and global rarity, the latter being defined using the global saliency of each vertex based on its contrast with respect to all other vertices. Yang et al (2009) calculate vertex saliency using its distance with respect to its neighborhood and include it in face saliency computation.…”

Section: Literature Reviewmentioning

confidence: 99%

Selectively densified 3D object modeling based on regions of interest detection using neural gas networks

2016

View full text Add to dashboard Cite

The paper discusses automated solutions for 3D object modeling at multiple resolutions in the context of virtual reality. An original solution, based on an unsupervised neural network, is proposed to guide the creation of selectively densified meshes. A neural gas network, applied over a sparse density object mesh, adapts its nodes during training to capture the embedded shape of the object. Regions of interest are then identified as areas with higher density of nodes in the adapted neural gas map. Meshes at different level of detail for an object, which preserve these regions of interest, are constructed by adapting a classical simplification algorithm, the QSlim. The simplification process will therefore only affect the regions of lower interest, ensuring that the characteristics of an object are preserved even at lower resolutions. Various interest point detectors are incorporated in selectively densified meshes in a similar manner to enable the comparison with the proposed neural gas approach. A novel solution based on learning is proposed to select the number of faces for the discrete models of an object at different resolutions. Finally, selectively densified object meshes are incorporated in a discrete level-of-detail method for presentation in virtual reality applications.

show abstract

Mesh saliency with global rarity

Cited by 77 publications

References 26 publications

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Local-to-global mesh saliency

Selectively densified 3D object modeling based on regions of interest detection using neural gas networks

Contact Info

Product

Resources

About