A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics.
The 3D human model is widely used in anthropometry, clothing design, virtual human animation, game, and so on. The corresponding 3D human point cloud model is obtained by the 3D scanner, and then the data is represented as a grid or a surface by method for 3D reconstruction. Model segmentation is the basis of shape analysis. Segmentation of human model is different from other 3D models because of the particularity of the human body; the segmentation of the human model should accord with the human body semantic knowledge. In order to obtain the semantic knowledge of the human body, such as the definition of semantic knowledge related to hand, arm, head, legs, and trunk, it is necessary to obtain the model’s feature points automatically, such as head vertex, neck point, and perineum point.
Geodesic distance is a widely used method during detecting human feature points. However, facing the model topology changes, such as the case of hands in hands, geodesic distance method is unable to detect the feature points of the hand. The method to determine the characteristics of human joints according to the proportion of the human body anthropometric value may lead to deviation because the human body model does not meet the standard ratio. Heat kernel signature is a method based on attribute representation model of LaplaceBeltrami operator and heat kernel function. It limits the variables in the time domain which can fully express geometric characteristics of the model. Also it has affine invariance and deformation invariance.
In this paper, we propose a segmentation method based on heat kernel signature and geodesic distance. Our key idea is to discover semantic feature points by computing the heat kernel signature of human model. We search the partial maxima of the thermal energy distribution of the model and obtained a set of feature points of the model by calculating the heat kernel signature of the point cloud of human body model. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the algorithm can be used to solve the feature points that geodesic distance method cannot defect. And the human body model with different postures can get the segmentation result which accords with the human semantic feature.
The paper is organized as follows. In Section
A large amount of work has been done on shape segmentation in the context of shape analysis. According to the similarity of image segmentation, the 3D model segmentation algorithm is based on the ideas and terminology of image segmentation algorithm in a great extent. At present, the main method of point cloud segmentation is as follows.
In addition to the above several methods, we have the classic model segmentation method as well as personal rank algorithm [
The characteristics of human body are similar to those of other kinds of 3D models. Some of the characteristics of the human body are related to changes in the partial geometric quantities (such as normal vector and curvature). However, unlike other types of models, some human characteristics are used in the measurement of garment CAD. Allen et al. [
HKS (heat kernel signature) is a method of using spectral theory to characterize the properties attribute of the model. The descriptor is derived from the heat transfer theory. It can fully express the geometric characteristics of the model and has affine invariance and deformation invariance. Heat kernel signature is widely used in 3D point cloud segmentation, retrieval, model reconstruction, and so on. In Section
In the research field of 3D point cloud model retrieval, it is used for multiscale matching of nonrigid shapes because the heat kernel signature has deformation invariance [
In the process of selecting feature points, the widely used method is geodesic distance. The fuzzy hierarchical segmentation algorithm proposed by Katz and Tal [
Heat kernel operator and heat kernel function are from the thermal diffusion theory of Riemann manifold. The heat equation is used to describe the variation of heat distribution with time. For compact Riemann manifolds
Given an initial heat distribution function
Paper [
For vertex
Heat kernel takes up most of heat kernel function’s merit property. But in order to reduce the complexity of heat kernel and reduce redundant information, it converts two vectors in heat kernel function into one, making its HKS more concise and easy to calculate and compare.
Heat kernel has many good geometric properties, such as invariant isometric transformation, affine invariant, multiple scale, and stability. The following are some of the main properties of heat kernel.
Heat kernel is invariant under isometric transformations. Given
From (
Due to the above two characteristics of heat kernel, it has nothing to do with attitude of the model. Therefore, heat kernel is widely used in model retrieval [
The vertex
The ADF [
In the analysis of algorithm experimental results of Section
Stability means heat kernel function is stable over the noise of model, insensible to weak disruption. Heat kernel
Both [
Heat kernel function
Let manifold
If
Characteristic system of
Equation (
According to different data processing needs, human body model has different semantic feature point definitions. Inside the human body model, human body level structures are basically decided by the semantic feature points of body parts such as perineum point and waist line point. Human body model semantic feature point definitions are shown in Table
Definition of human semantic feature points.
Feature points number  Located part  Feature points’ semantic and meaning 


Limbs  Left hand 

Left elbow  

Left underarm  

Right hand  

Right elbow  

Right underarm  

Left foot  

Left knee  

Right foot  

Right knee  



Body  Perineum point 

Waist line point  

Neck 
Definition of semantic feature point.
According to the definition of human body model semantic feature point from Table
Segmentation structure of the human body.
Level division order  Curved surface names  Meanings  Corresponding feature points 

(1) 

Left lower arm 

(2) 

Left upper arm 

(3) 

Right lower arm 

(4) 

Right upper arm 

(5) 

Left lower leg 

(6) 

Left thigh 

(7) 

Right lower leg 

(8) 

Right upper leg 

(9) 

Buttock 

(10) 

Upper body 

(11) 

Head 

Feature point detection is a very important process in the fields of partition, simplification, rebuild, and retrieval for 3D model. We will give definition to an expanded HKS descriptor.
Let
As the analysis of the features of heat kernel in Section
The result of HKS in the case of different diffusion time
When the time
The result of feature point extraction in the case of different diffusion times
According to the above analysis, the detailed arithmetic that uses the heat kernel signature to detect the human body model feature points is the following.
Choose
According to (
Descend order
Set threshold value
Choose many
Remove the redundant points from the feature points set, and pick out the human body semantic feature points which correspond with the definitions in Table
After we get the human body model semantic feature points, we use the geodesic distance method to divide the levels in human body model. The definition of geodesic distance is the length of the shortest path between two points that are on the surface of a body [
Segmentation of the human body model by the order of the minor parts of the curved surface is defined in Table
The following is the detailed arithmetic which used the geodesic distance to divide the levels:
Initialize the triangular set
Collect feature points
According to (
According to the order of human body level division that is defined in Table
According to the order of human body level division that is defined in Table
The arithmetic in this article is on a computer featured with 1.60 GHz CPU and 4.0 GB memory space; the tool that is used is the MATLAB software.
The input of the arithmetic in this article is the triangular net model; the experiment data is mainly the human body model from the data base TOSCA. In order to apply KNN arithmetic to get the
The result of feature point extraction in the case of different neighbor number
Let the number of feature points extracted be
The result of feature point extraction in the case of different number
The shape of the model using the ADF descriptor which is produced from expanding in the HKS method is described in [
Let
Comparison result of feature point extraction. (a) The result of methods in [
Using the method of literature [
Change of geodetic distance of model.
Figure
Results of feature points extraction from human model in different attitudes.
Clustering method is one of the common methods for 3D model segmentation.
Different gesture of the human body model segmentation results using
According to the semantic features of the four kinds of gestures in Figure
Segmentation results of the human body model under different postures using heat kernel and geodesic distance.
In this paper, we introduce a method of human model segmentation based on feature points. The local maxima of thermal energy distribution of the model are found by calculating the heat kernel signature of the point clouds of human body model. A set of feature points of human model under different postures is obtained. Select the feature points which accord with the semantic features of human body and realize the hierarchical segmentation of human model by calculating geodesic distance between feature points. We can detect different postures of the human model feature points through the experimental analysis. The segmentation results of human model based on feature points are consistent with the definition of the semantic features of the model. However, there are some problems in the algorithm proposed in the literature. For example, in the process of feature extraction, the amount of feature points extracted from the human model is large, but most of the feature points are meaningless. We only selected thirteen of them for human segmentation. Therefore, how to improve the accuracy of feature points extraction and reduce the redundancy of feature points is the research content in the future.
The authors declare that they have no conflicts of interest.