34-Issue 7

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/13266

Browse

Browsing 34-Issue 7 by Subject "I.3.5 [Computer Graphics]"

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    An Efficient Feathering System with Collision Control
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Liu, Le; Li, Xiaosheng; Chen, Yanyun; Liu, Xuehui; Zhang, Jian J.; Wu, Enhua; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    We present an efficient interactive system for dressing a naked bird with feathers. In our system, a skeleton associated with guide feathers is used to describe the distribution of the body feathers. The special skeleton can be easily built by the user, given a 3D bird model as input. To address the problem of interpenetrations among feathers, the growth priority between the feather roots is defined, with which we obtain the growth order from a greedily constructed directed acyclic graph. Each feather is then adjusted in that order by a height field based collision resolution process. The height field not only provides an efficient way to detect the collision but also enables us to finely control the degree of collision during feather adjustments. The results show that our approach is capable of resolving the collisions among thousands of feathers in a few seconds. If model animation is desired, the feathers can be adjusted on the fly at interactive framerates. Details of our implementation are provided with several examples to demonstrate the effectiveness of our system.
  • No Thumbnail Available
    Item
    Guided Mesh Normal Filtering
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Zhang, Wangyu; Deng, Bailin; Zhang, Juyong; Bouaziz, Sofien; Liu, Ligang; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is evaluated using a guidance signal instead of the original signal. It has been successfully applied to various image processing problems, where it provides more flexibility than the standard bilateral filter to achieve high quality results. On the other hand, its success is heavily dependent on the guidance signal, which should ideally provide a robust estimation about the features of the output signal. Such a guidance signal is not always easy to construct. In this paper, we propose a novel mesh normal filtering framework based on the joint bilateral filter, with applications in mesh denoising. Our framework is designed as a two-stage process: first, we apply joint bilateral filtering to the face normals, using a properly constructed normal field as the guidance; afterwards, the vertex positions are updated according to the filtered face normals. We compute the guidance normal on a face using a neighboring patch with the most consistent normal orientations, which provides a reliable estimation of the true normal even with a high-level of noise. The effectiveness of our approach is validated by extensive experimental results.
  • No Thumbnail Available
    Item
    Mesh Denoising using Extended ROF Model with L1 Fidelity
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Wu, Xiaoqun; Zheng, Jianmin; Cai, Yiyu; Fu, Chi-Wing; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    This paper presents a variational algorithm for feature-preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge-lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm.
  • No Thumbnail Available
    Item
    Object Completion using k-Sparse Optimization
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Mavridis, Pavlos; Sipiran, Ivan; Andreadis, Anthousis; Papaioannou, Georgios; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    We present a new method for the completion of partial globally-symmetric 3D objects, based on the detection of partial and approximate symmetries in the incomplete input dataset. In our approach, symmetry detection is formulated as a constrained sparsity maximization problem, which is solved efficiently using a robust RANSACbased optimizer. The detected partial symmetries are then reused iteratively, in order to complete the missing parts of the object. A global error relaxation method minimizes the accumulated alignment errors and a nonrigid registration approach applies local deformations in order to properly handle approximate symmetry. Unlike previous approaches, our method does not rely on the computation of features, it uniformly handles translational, rotational and reflectional symmetries and can provide plausible object completion results, even on challenging cases, where more than half of the target object is missing. We demonstrate our algorithm in the completion of 3D scans with varying levels of partiality and we show the applicability of our approach in the repair and completion of heavily eroded or incomplete cultural heritage objects.
  • No Thumbnail Available
    Item
    Projective Feature Learning for 3D Shapes with Multi-View Depth Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Xie, Zhige; Xu, Kai; Shan, Wen; Liu, Ligang; Xiong, Yueshan; Huang, Hui; Stam, Jos and Mitra, Niloy J. and Xu, Kun
    Feature learning for 3D shapes is challenging due to the lack of natural paramterization for 3D surface models. We adopt the multi-view depth image representation and propose Multi-View Deep Extreme Learning Machine (MVD-ELM) to achieve fast and quality projective feature learning for 3D shapes. In contrast to existing multiview learning approaches, our method ensures the feature maps learned for different views are mutually dependent via shared weights and in each layer, their unprojections together form a valid 3D reconstruction of the input 3D shape through using normalized convolution kernels. These lead to a more accurate 3D feature learning as shown by the encouraging results in several applications. Moreover, the 3D reconstruction property enables clear visualization of the learned features, which further demonstrates the meaningfulness of our feature learning.