A Magic Wand Selection Tool for Surface of 3D Model
Authors: Liu Bangquana , Zhu Shaojun , Sun Dechaoc , Zhou GuangYua , Yang Weihua , Liu Li , Chen Kai 
Published: September 2020

Introduction:

segmentation of 3d shapes is a fundamental problem in computer graphics and computer-aided design. It has
received much plays 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 it an important role in a wide range of
applications, including mesh parameterization, skeleton extraction, resolution modeling, shape retrieval, character
recognitio,, 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 user input some stokes near the edge of the selected or segmented region. Chen
et al.introduced an approach to join the specified points 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 use 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:

We implemented our algorithm in C++ and Open MP and conducted the experiments on a PC with a 3.07 GHz
Intel(R) Core(TM) i7 CPU and 6 GB memory. 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 kitten model at two different interaction points, from which we can see the obtained regions are similar from
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 interactive select surface of 3D model. The
feature vector is constructed by extracting the HKS feature descriptor and means curvature of 3D model surface, which
allows users to input the feature tolerance value for region selection and improves 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, we propose a novel magic wand selection tool 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 we provide two
ways for region selection. 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.