Mesh Saliency Detection Based on Entropy

Tao, Pingping; Zhang, Lina; Cao, Junjie; Liu, Xiuping

doi:10.1109/icdh.2016.065

Cited by 5 publications

(8 citation statements)

References 18 publications

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“…θ = 5.68 o HD = 0.002054 θ = 9.79 o HD = 0.003241 (f) (g) Fig. 9: Heatmaps of saliency mapping and denoising results using the methods: (a) curvature co-occurrence histogram [9], (b) entropy based salient model [11], (c) a mesh saliency [28], (d) mesh saliency via spectral processing [12], (e) Point-wise saliency detection [29], (f) mesh saliency via CNN [30], (g) our approach.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Wei et al [9] presented a 3D saliency mapping mechanism using the curvature cooccurrence histogram, following similar steps with the method proposed in [10] for extracting salient image features. Tao et al [11] proposed an entropy-based saliency approach using the entropy of the normals to depict the local changes in a region. Song et al [12] proposed a method which incorporates global considerations by making use of spectral attributes.…”

Section: Recent Workmentioning

confidence: 99%

“…In this way, this method could be used as a pre-processing step for the creation of a digital replica of the original cultural object, without imperfections, since it highlights the areas that need repair (i.e., digital repairing is also available). The recent 4 "Calcite Vase" model 5 "Epichysis 26332 b" model [11], (e) mesh saliency via spectral processing [12], (f) the proposed method.…”

Section: E Utilizing 3d Saliency Mapping In Industrial Applicationsmentioning

confidence: 99%

“…(a) Original model, and heatmaps visualization of saliency mapping based on: (b) the eigenvalues of small patches (spectral analysis), as described in paragraph IV-B, (c) the RPCA approach (geometrical analysis), as described in paragraph IV-A, (d) Wei et al[9], (e) Tao et al[11], (f) Lee et al[28], (g) Song et al[12], (h) Guo et al[29], (i) Song et al (CNN)[30], (j) our approach.…”

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confidence: 99%

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Robust and Fast 3-D Saliency Mapping for Industrial Modeling Applications

Arvanitis

Lalos

Μουστάκας

2021

IEEE Trans. Ind. Inf.

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New generation 3D scanning technologies are expected to create a revolution at the Industry 4.0, facilitating a large number of virtual manufacturing tools and systems. Such applications require the accurate representation of physical objects and/or systems achieved through saliency estimation mechanisms that identify certain areas of the 3D model, leading to a meaningful and easier to analyze representation of a 3D object. 3D saliency mapping is, therefore, guiding the selection of feature locations and is adopted in a large number of low-level 3D processing applications including denoising, compression, simplification and registration. In this work, we propose a robust and fast method for creating 3D saliency maps that accurately identifies sharp and small scale geometric features in various industrial 3D models. An extensive experimental study using a large number of 3D scanned and CAD models, verifies the effectiveness of the proposed method as compared to other recent and relevant approaches despite the constraints posed by complex geometry patterns or the presence of noise. Index Terms-3D Mesh saliency mapping, industrial modeling & applications, spectral & geometric analysis for vertex saliency.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Recent Workmentioning

confidence: 99%

Section: E Utilizing 3d Saliency Mapping In Industrial Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Robust and Fast 3-D Saliency Mapping for Industrial Modeling Applications

Arvanitis

Lalos

Μουστάκας

2021

IEEE Trans. Ind. Inf.

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“…Consequently, in the area of geometry processing, 3D mesh saliency, as a measure of regional importance of 3D meshes, was coined by Lee et al [7]. Several other approaches followed utilizing spectral methods [8,9], curvature-based methods [7,10] multiscale descriptors [11], entropy-based methods [12] and hybrid methods [13] taking into account both geometry and color.…”

Section: Introductionmentioning

confidence: 99%

Mesh Saliency Detection Using Convolutional Neural Networks

Nousias

Arvanitis

Lalos

et al. 2020

2020 IEEE International Conference on Multimedia and Expo (ICME)

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Mesh saliency has been widely considered as the measure of visual importance of certain parts of 3D geometries, distinguishable from their surroundings, with respect to human visual perception. This work is based on the use of convolutional neural networks to extract saliency maps fo large and dense 3D scanned models. The network is trained with saliency maps constructed with a fusion spectral and geometrical analysis generated measures. Extensive evaluation studies carried out, include visual perception evaluation, simplification and compression use cases. As a result, they verify the superiority of our approach as compared to other state-of-theart approaches. Furthermore, performance experiments indicate that CNN-based saliency extraction method is much faster in large and dense geometries allowing its application in low-latency and energy-efficient systems. Index Terms Saliency mapping extraction, Convolutional Neural Network, saliency features of 3D objects.

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