一种用最大法线分布方向修正CPCA主轴的方法

A CPCA Principal Axes Corrected Approach Using the Direction of Maximum Normal Distribution

为了得到更合理的用于旋转归一化的主轴,综合利用三维模型的顶点位置和法线方向两种表面特性,提出一种用最大法线分布方向修正CPCA主轴的MNCPCA方法(maximum normal corrected PCA).为了增强算法的鲁棒性,首先借助一组法线参考方向来统计三维模型的法向分布直方图,避免了计算误差和错误法向的影响,再依据该直方图分析模型是否具有显著的最大法线分布方向.进而,在此基础上给出CPCA主轴的修正策略.实验结果表明,与CPCA方法相比,MNCPCA方法得到的主轴更符合人的认知习惯.

PCA-alignment only captures the relative location of 3D models＇ surface vertexes, and thus does not provide robust rotation normalization. In order to get more reasonable axes for the rotation normalization of 3D models, an approach to correct the principal axes by CPCA approach is proposed in this paper, using maximum normal distribution. The approach is called MNCPCA （maximum normal corrected PCA） for short. Two appearance＇s attributes which are vertexes position coordinates and normal directions are considered in the approach. To improve the robustness of the algorithm, firstly a group of referenced normals which are obtained by uniform sampling is used to compute normal distribution histogram, in order to avoid error in numerical calculation and incorrect normal; next normal distribution characteristic of 3D models is analyzed, judging whether the 3D model has an obvious maximum normal distribution by the normal distribution histogram. And then, based on the analysis, a strategy to correct CPCA principal axes is presented, only adjusting the principal axes for some 3D models which have obvious maximum normal distribution, and for the other models, the principal axes by CPCA is still unchanged. Experimental results show that the MNCPCA approach can get more reasonable axes than CPCA approach by human cognition.