Mesh resizing based on hierarchical saliency detection

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.

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“…Likewise, shape matching [5], mesh resizing [6], and face recognition [7] can take advantage from saliency detection.…”

Section: Introductionmentioning

confidence: 99%

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

Nouri

Charrier

Lézoray

2015

Signal Processing: Image Communication

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“…Significant research effort has gone into this area in order to easily place 3D models into different scenes. Miao and Lin [30] constructed a quadratic energy function, that incorporated an edge sensitivity measure, to help guide salient feature-preserving model resizing, Jia et al [31] designed a region-based descriptor to compute the saliency of each region based on its contrast to neighboring regions and a hierarchical method for computing saliency. They showed that by optimizing a global energy function on the mesh, visually appealing mesh resizing results can be obtained.…”

Section: Content Aware Resizingmentioning

confidence: 99%

Intelligent Visual Media Processing: When Graphics Meets Vision

Cheng

Hou

Zhang

et al. 2017

J. Comput. Sci. Technol.

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The computer graphics and computer vision communities have been working closely together in recent years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.

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“…The human visual system (HVS) can process large amounts of visual information instantly and is able to locate objects of interest and distinguish them from complex background scenes. Research shows that the HVS pays more attention to infrequent features and suppresses repetitive ones [1][2]. Visual saliency plays an important role in the process in which HSV identifies scenes and detects objects.…”

Section: Introductionmentioning

confidence: 99%

“…As for computer graphics, while the concept of visual saliency has been widely explored for mesh saliency [6], [8][9][10][11][12][13][14][15][16][17][18], few studies have explored visual saliency in point clouds [19][20][21][22]. Visual saliency is an important topic for 3D surface study and has important applications in 3D geometry processing such as resizing [1], simplification [23][24], smoothing [25], segmentation [26], shape matching and retrieval [27][28], 3D printing [29], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Point cloud saliency detection via local sparse coding

Narváez

Sánchez-Torres

Bedoya

2019

DYNA

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The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates adescriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle.Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review.

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Mesh resizing based on hierarchical saliency detection

Cited by 12 publications

References 12 publications

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

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

Intelligent Visual Media Processing: When Graphics Meets Vision

Point cloud saliency detection via local sparse coding

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