对于已经提取的三维场景对象，再使用K-means聚类算法对其进行可见性

对于已经提取的三维场景对象，再使用K-means聚类算法对其进行可见性的判定。
K-means聚类算法通过对各个样品和各个聚类中心之间的距离会籍的平方，然后将类平方 增加到类和函数的误差平方的加权和误差目标函数 ：

結果 (英文) 1:

Scene object that has been extracted, and then using the k-means clustering algorithm to determine the visibility of its.All sample and k-means clustering algorithm based on clustering distance between the Centre of the square and square of the classes into classes and functions of a weighted sum of square error error objective function:

結果 (英文) 2:

For the 3D scene objects have been extracted, and then use K-means clustering algorithm determining its visibility.
K-means clustering algorithm through the square of the distance of each sample and each membership between cluster centers, and then squared error class to class and function of the square of the weighted objective function and error:

結果 (英文) 3:

For the 3D scene objects have been extracted, and then use the K-means clustering algorithm to determine the visibility of its.
K-means clustering algorithm based on between each sample and each cluster center distance membership square, then the class of square added to the weighted square error and error objective function:
classes and functions.