ABSTRACT 3D mesh segmentation, as a very challenging problem in computer graphics, has attracted considerable interest. The most popular methods in recent years are data‐driven methods. However, such methods require a large amount of accurately labeled data, which is difficult to obtain. In this article, we propose a novel mesh segmentation algorithm based on anisotropic sparse embedding. We first over‐segment the input mesh and get a collection of patches. Then these patches are embedded into a latent space via an anisotropic ‐regularized optimization problem. In the new space, the patches that belong to the same part of the mesh will be closer, while those belonging to different parts will be farther. Finally, we can easily generate the segmentation result by clustering. Various experimental results on the PSB and COSEG datasets show that our algorithm is able to get perception‐aware results and is superior to the state‐of‐the‐art algorithms. In addition, the proposed algorithm can robustly deal with meshes with different poses, different triangulations, noises, missing regions, or missing parts.

ABSTRACT Most of point cloud segmentation methods are based on clean datasets and are easily affected by label noise. We present a novel method called Joint‐learning, which is the first attempt to apply a dual‐network framework to point cloud segmentation with noisy labels. Two networks are trained simultaneously, and each network selects clean samples to update its peer network. The communication between two networks is able to exchange the knowledge they learned, possessing good robustness and generalization ability. Subsequently, adaptive sample selection is proposed to maximize the learning capacity. When the accuracies of both networks are no longer improving, the selection rate is reduced, which results in cleaner selected samples. To further reduce the impact of noisy labels, for unselected samples, we provide a joint label correction algorithm to rectify their labels via two networks' predictions. We conduct various experiments on S3DIS and ScanNet‐v2 datasets under different types and rates of noises. Both quantitative and qualitative results verify the reasonableness and effectiveness of the proposed method. By comparison, our method is substantially superior to the state‐of‐the‐art methods and achieves the best results in all noise settings. The average performance improvement is more than 7.43%, with a maximum of 11.42%.

Abstract We introduce a registration‐based propagation strategy to extract temporally coherent skeletons from the animated surfaces. We first extract complete skeletons for the key frames of the animated surfaces, and present a spatio‐temporal L1‐medial skeleton extraction method to extract the initial skeletons for the immediate frames between the key frames. As these initial skeletons may not be complete, we then develop a global skeleton alignment method, which effectively warps the key skeleton to these initial skeletons subsequently. By using the geometry merging, we propagate the geometry of the warped key skeleton to the initial skeletons, and produce temporally coherent skeletons for the input animated surfaces. Our system can be applied to the raw scanned animated object and requires only minimal user interaction to extract complete and temporally coherent animated skeletons. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT It is very difficult to deform flexible objects in computer animation. This paper presents a novel approach to address this problem. A detail‐sensitive and deformation‐sensitive simplification is first conducted on the original mesh. The simplified mesh is then deformed, and this deformation is transferred to the original mesh to produce an initial result. Because of the discontinuity between some vertices, an as‐rigid‐as‐possible optimization is employed to prevent the shape distortion and control the surface stiffness. Various experimental data demonstrate that our algorithm is intuitive, efficient, and effective in deforming large meshes. Copyright © 2014 John Wiley & Sons, Ltd.

This paper presents an efficient and physics-plausible deformation algorithm. A simplification is first conducted on the original mesh, then the deformation of the simplified mesh is passed to the original mesh to obtain an initial result. Then a set of optimal rigid transformations are computed to preserve the shape features and control the surface stiffness. The experiments show that our algorithm is effective.

ABSTRACT Existing deformation techniques are oblivious to salient structures that often capture the essence of 3D meshes. Combining with gradient domain technique, we propose an alternative approach to preserve these structures in the volumetric subspace. Through a simple sketching interface, the structures of the input mesh are specified by the user. During deformations, these key structures are constrained to deform rigidly to maintain their original shapes, hence avoiding serious visual artifacts. However, this process leads to a nonlinear optimization problem. To guarantee fast convergence as well as numerical stability, we project the deformation energy onto a volumetric subspace which envelops the input mesh. Then the energy optimization is performed in this subspace to greatly facilitate the editing of large meshes. Massive experimental data demonstrate the effectiveness and efficiency of our algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Creating an attractive mesh animation is a laborious and time‐consuming task. In this paper, we propose a practical deformation transfer algorithm to make it easier. To achieve a robust numerical solver, we perform the transfer process in the dual domain; that is, the deformations are transferred between the dual meshes of the source and target meshes. Firstly, the source animation is analyzed and visualized to help the user specify markers in the large deformation regions. Then, through respecting the coherence information, a fast and deformation‐aware surface correspondence approach is presented to determine how the source animation is transferred. Finally, the transferred result can be reconstructed via dual Laplacian optimization. Various experimental results demonstrate the effectiveness and applicability of this paper. Moreover, a user study is carefully designed to perceptually validate our motivation and advantages. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose a robust deformation transfer algorithm. Unlike previous work, the transfer process is performed in the dual domain, i.e., the deformations are transferred between the dual meshes of the source and target meshes. Firstly, the source mesh sequence and the target mesh are converted to their dual meshes. Then the source animation is analyzed and represented as a set of affine transformations. After the surface correspondence is built, the transferred result can be reconstructed by dual Laplacian optimization. The experimental results demonstrate that our algorithm is simple but very effective.

Abstract It is a fundamental but challenging problem to efficiently edit complex 3D objects. By embedding the input models into coarse tetrahedral control meshes, this paper develops a unified framework to discuss two useful editing operations: interactive deformation and deformation transfer. First, a new rigidity energy is proposed to make the tetrahedral control mesh deform as rigidly as possible, which yields intuitive detail and volume preservation even under large deformations. And an error‐driven refinement approach is presented to further improve the deformation result. Then, based on this deformation scheme, a volumetric correspondence method is introduced to perform the deformation transfer task between the tetrahedral control meshes of the source and target models, which greatly lessens the burden of the user. Experimental results show our algorithm is effective, easy to control, supports various shape representations, and well transfers deformations between non‐homeomorphous models. Copyright © 2009 John Wiley & Sons, Ltd.