Abstract
Introduction: Segmentation of 3d shapes is a fundamental problem in computer graphics and computer-aided design. It has received much attention in recent years. The analysis and research methods of 3d mesh models have established reliable mathematical foundations in graphics and geometric modeling. Compared with color and texture, shape features describe the shape information of objects from geometric structure features and play an important role in a wide range of applications, including mesh parameterization, skeleton extraction, resolution modeling, shape retrieval, character recognition, robot navigation, and many others.
Methods: The interactive selection surface of models is mainly used for shape segmentation. The common method is boundary-based selection, which requires the user to input some stokes near the edge of the selected or segmented region. Chen et al. introduced an approach to join the specified points to form the boundaries for region segmentation on the surface. Funkhouser et al. improve the Dijkstra algorithm to find segmentation boundary contour. The graph cut algorithm uses the distance between the surface and its convex hull as the growing criteria to decompose a shape into meaningful components. The watershed algorithm, widely used for image segmentation, is a region- growing algorithm with multiple seed points. Wu and Levine use simulated electrical charge distributions over the mesh to deal with the 3D part segmentation problem. Other methods using a watershed algorithm for surface decomposition. Results: Our algorithm in C++ and Open MP has been implemented and the experiments on a PC with a 3.07 GHz Intel(R) Core(TM) i7 CPU and 6 GB memory have been conducted. Our method can get a similar region under different interaction vertices in specific regions. Figure 6a and Figure 6b are the calculation results of tolerance region selection of this algorithm in a certain region of the kitten model at two different interaction points, from which it has been observed that the obtained regions are similar to different vertices in this region. Figure 6c and Figure 6d are two different interactive points in the same region, and the region selection results are obtained by Region growing technique. Discussion: In this paper, we proposed a novel magic wand selection tool to the interactive select surface of the 3D model. The feature vector is constructed by extracting the HKS feature descriptor and mean curvature of 3D model surface, which allow users to input the feature tolerance value for region selection and improve the self-interaction of users. Many experiments show that our algorithm has obvious advantages in speed and effectiveness. The interactive generation of region boundary is very useful for many applications, including model segmentation. Conclusion: In consideration of a couple of requirements, including user-friendliness and effectiveness in model region selection, a novel magic wand selection tool has been proposed to interactive selection surface of 3D models. First, we pre-compute the heat kernel feature and mean curvature of the surface, and then form the eigenvector of the model. Then, two ways for region selection have been provided. One is to select the region according to the feature of tolerance value. The other is to select the region that aligns with stroke automatically. Finally, we use the geometry optimization approach to improve the performance of the computing region con-tours. Extensive experimental results show that our algorithm is efficient and effective.Keywords: Magic wand, geometry optimization, region selection, heat kernel feature, mean curvature.
Graphical Abstract
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