Improved Laplacian Smoothing of Noisy Surface Meshes

Vollmer, J.; Mencl, Robert; Müller, Heinrich

doi:10.1111/1467-8659.00334

Cited by 420 publications

(247 citation statements)

References 9 publications

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“…A level of 10% injects noise into only 10% of the total number of vertices in the model. The HC Laplacian smoothing operation is performed using the algorithm in [15]. The cropping operation removes parts of the model along the x, y, or z planes in order to damage the watermark.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Using Radial Basis Function Networks for Watermark Determination in 3D Models

Motwani

Harris

2009

2009 Annual IEEE India Conference

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Abstract-In this paper, we investigate the ability of a radial basis function network to determine the values of the watermark to be inserted in a 3D triangulated mesh model. The challenge in a watermarking algorithm is to achieve high watermark embedding capacity without causing perceptual distortion to the model. The proposed technique overcomes this challenge. The principal, mean and Gaussian curvature values computed at each vertex of the 3D model collectively represent the local geometry of the vertex and its neighborhood. These curvature values are used as input feature vectors to train the neural network. The amount of watermark to be inserted at a vertex is non-linearly proportional to these feature vectors. The neural network is trained with watermark values to learn this non-linear relationship. A wide range of 3D models are used to train the neural network such that the large variations in the geometry of the vertices is incorporated by the training phase. The algorithm adopts a non-blind extraction process to retrieve the watermark. Experimental results prove that the watermarking algorithm achieves imperceptibility, high capacity and robustness to noise and cropping attacks up to 20% level.

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Section: Experiments and Resultsmentioning

confidence: 99%

Using Radial Basis Function Networks for Watermark Determination in 3D Models

Motwani

Harris

2009

2009 Annual IEEE India Conference

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“…We achieve this objective by a standard application of Laplacian smoothing as in [13], which minimizes the curvature of the surface, and where at each update, every point is redefined to be a weighted average of itself and its neighbors. At each iteration k over the mesh of 3D points X, we update…”

Section: Methodsmentioning

confidence: 99%

Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

Jorstad

Fua

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. We present an active surface-based method for refining the boundary surfaces of mitochondria segmentation data. We exploit the fact that mitochondria have thick dark membranes, so referencing the image data at the inner membrane can help drive a more accurate delineation of the outer membrane surface. Given the initial boundary prediction from a machine learning-based segmentation algorithm as input, we compare several cost functions used to drive an explicit update scheme to locally refine 3D mesh surfaces, and results are presented on electron microscopy imagery. Our resulting surfaces are seen to fit very accurately to the mitochondria membranes, more accurately even than the available hand-annotations of the data.

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“…Therefore, the correction process will lead to obvious deformation and shrinkage. To solve this problem, Taubin [8], Vollmer [7] and Desbrun [9] improved the Laplacian algorithm. Among these improved methods, Taubin's method is based on a transfer function of the filter as shown in Equation (3).…”

Section: Mesh Filteringmentioning

confidence: 99%

Three-dimensional Modeling of Carbon Fiber Cloth Surface from Dense and Scattered Points Cloud

Cheng¹,

Li²

2015

TOMSJ

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Fabric surface analysis, as part of fabric analysis, is very important for the textile manufacturing process and is traditionally based on human-labor or image processing which is a conventional automatic method. However, image quality is influenced by ambient light, background light and optical properties of the surface. In this paper, we present a three dimensional modeling techniques for the reconstruction of carbon-fiber fabric surface. Firstly, a dense and scattered points cloud is collected using 3D laser scanning system after necessary data quality analysis. Secondly, the original points cloud is preprocessed according to classification. Finally, three-dimensional fabric surface model is reconstructed using screened Poisson reconstruction algorithm. The experimental results show that the reconstructed model is acceptable.

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Improved Laplacian Smoothing of Noisy Surface Meshes

Cited by 420 publications

References 9 publications

Using Radial Basis Function Networks for Watermark Determination in 3D Models

Using Radial Basis Function Networks for Watermark Determination in 3D Models

Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

Three-dimensional Modeling of Carbon Fiber Cloth Surface from Dense and Scattered Points Cloud

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