Adaptive Patches for Mesh Denoising

Hurtado, Jan; Gattass, Marcelo; Raposo, Alberto; Coêlho, Jéferson R. P.

doi:10.1109/sibgrapi.2018.00007

Cited by 8 publications

(14 citation statements)

References 20 publications

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“…Li et al [12] utilized the edge-based neighborhood to preserve sharp features. Hurtado et al [13] selected the neighborhood by considering the gradient information in addition to the normal information. The local iterative method can gradually recover the features well, and the calculation efficiency is high.…”

Section: A Local Methodsmentioning

confidence: 99%

Adaptive and Feature-Preserving Mesh Denoising Schemes Based on Developmental Guidance

Yue

et al. 2020

IEEE Access

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Distinguishing among different kinds of features as well as noises on 3D mesh models is crucial for feature-preserving mesh denoising. This paper proposes to address this issue via in-depth analysis of the intermediate products of the denoising processes, and one framework is presented for raising adaptive and feature-preserving mesh denoising schemes. Firstly, by analyzing the changes of the facet normals during the denoising process, we propose the definition of developmental guidance, which helps to assess the current filtering status and predict the positions of feature and smooth regions. Then, by incorporating the guidance, we put forward one interpolation-based denoising scheme, which affords an efficient way to interpolate and recover different levels of features and is robust to severe noises. Besides, we also introduce the guidance to the optimization-based model, and the achieved global scheme is tested to be stable and robust to irregular samplings. Both the theoretical analysis and extensive experimental results on synthetic and real-world noises have demonstrated the attractive advantages of our whole framework, such as being adaptive, efficient, robust, feature-preserving, etc. INDEX TERMS bilateral filtering, feature-preserving, guided filter, linear interpolation, mesh denoising

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Section: A Local Methodsmentioning

confidence: 99%

Adaptive and Feature-Preserving Mesh Denoising Schemes Based on Developmental Guidance

Yue

et al. 2020

IEEE Access

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“…Li et al [16] preserved sharp features through edge-based neighborhood processing. Hurtado et al [17] considered gradient, point, and normal information to select and process neighborhoods. Overall, local methods can recover model features iteratively.…”

Section: Local Methodsmentioning

confidence: 99%

Adaptive and Feature-Preserving Bilateral Filters for Three-Dimensional Models

Li¹,

Yue²,

Jiang³

2020

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Bilateral filtering is a well-known tool to denoise or smooth one-dimensional (1D) signals, two-dimensional (2D) images, and three-dimensional (3D) models. The bilateral weights help preserve the edges or features more effectively than unilateral weights. However, it is immensely difficult to configure the scale parameters of the convolutional kernel functions. To overcome the difficulty, this paper proposes adaptive, feature-preserving bilateral filters for 3D models by introducing automatic, adaptive scale parameters. Firstly, the feature scale was defined on 3D models, bridging up the gap between feature scale and scale parameter of the Gaussian functions. Next, the scale descriptor was proposed to adaptively configure the scale parameter for each face of the target 3D model, changing the traditional approach of adopting the same scale parameter for all faces. On this basis, a feature-preserving local filter was designed by introducing the adaptive scale parameters to the iterative local scheme, and a modified global filter, which is robust to irregular sampling during denoising, was designed based on the adaptive scale parameters. The excellence of our filters was proved through experiments on multiple synthetic and real-world noisy models, in comparison to the state-of-the-art filters. The research results lay a solid basis for feature preservation and noise removal of 3D models.

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“…算法 [1][2][3][4][5][6][7][8][9][10][11][12] , 其特点是能够较好地利用模型的邻域信息来描述模型局部细节特征. 局部算法需要将卷积操作扩散到全模型, 但是依赖于较多次数的迭代.…”

Section: 在三维去噪问题中最早提出的是局部迭代unclassified

“…Liu 等 [7] 在指导滤波 [4] 的基础上, 通过特征探测识别特征面与非特征面, 并提出邻域选择的度量来增强特征, 取得比指导滤波更好的效果. 滤波过程中对邻域面片进行选择 [2,[8][9] 的处理同样可以提升效果. Sun 等 [2] 根据三角形面片与邻域面片法向角度选择邻域, Li 等 [8] 在滤波过程中对尖锐特征使用基于边的邻域处理, Hurtado 等 [9] 选择邻域除了点和法向信息外考虑梯度信息来处理邻域.…”

Section: 在三维去噪问题中最早提出的是局部迭代unclassified

“…滤波过程中对邻域面片进行选择 [2,[8][9] 的处理同样可以提升效果. Sun 等 [2] 根据三角形面片与邻域面片法向角度选择邻域, Li 等 [8] 在滤波过程中对尖锐特征使用基于边的邻域处理, Hurtado 等 [9] 选择邻域除了点和法向信息外考虑梯度信息来处理邻域. 局部算法能够通过迭代很好地逐渐恢复特征, 但为了保持细节特征, 局部算法的迭代次数往往很难自动设定.…”

Section: 在三维去噪问题中最早提出的是局部迭代unclassified

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