局部特征熵的网格非均匀简化算法

针对三维模型简化后的精度与效率上难以平衡的问题进行研究，提出一种局部特征熵的半边折叠非均匀网格简化算法。采用两次局部区域聚类探测，首先探测三维数据点所在边聚类局部区域，获取该探测区域法向量，其次以三维数据点临近点区域的重心约束来探测二次聚类区域法向量；根据信息熵的定义利，用两次探测的法向量之间夹角信息构建局部区域特征熵值做为半边折叠的代价，局部区域特征熵越大表示该区域越趋于平面，应优先简化，否则当保留；最后采用三角形内角判断方法来保留简化后网格中三角形的正则度，以减小变形引起的误差。实验结果表明，本算法在三维模型分均匀简化中在局部细节特性精度上和时间效率上能达到较优的平衡。

Local feature entropy based mesh non-uniform simplification algorithm

Aiming to solve the issue that the accuracy and efficiency after the simplification of 3D model is difficult to be balanced, a new method of simplified algorithm based on half-edge collapse nonhomogeneous mesh method of local characteristic entropy is proposed. Detect clustering local area twice. Firstly, detect edge clustering local area where there is 3D data point to obtain normal vector in the area; secondly, detect the normal vector of secondary regional clustering area by the constraints of the center of gravity of the region near 3D data points. According to the definition of information entropy, take the local area characteristic entropy constructed by angle information between the two normal vectors from the two detection method as the half edge collapse cost. The bigger the local area characteristic entropy is, the flatter the region tends to be, and the priority of simplification shall be given to this, otherwise it shall be retented. Lastly, retain the triangle regularity in the simplified mesh judged by the interior angles to reduce the deformation caused by the error. The experimental results show that the algorithm can achieve a better balance in the accuracy and time efficiency of the local details.