点模型的几何图像简化法  

Simplification of Point-Sampled Model Based on Geometry Images

作　　者：王仁芳[1] 张三元[1] 叶修梓[1] 

机构地区：[1]浙江大学计算机科学与技术学院,杭州310027

出　　处：《计算机辅助设计与图形学学报》2007年第8期1022-1027,共6页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(60333010;60473106);教育部博士点基金(20060335114)

摘　　要：提出一种基于几何图像的曲率自适应点模型简化算法.首先将点模型的球面极坐标映射到平面上,构造其几何图像;然后利用几何图像确定点模型中点的k-最近邻域及其曲面变分;最后结合曲面变分和简化密度对点集曲面重采样,并通过移动最小二乘曲面评估简化的误差.实验结果表明,该算法执行速度快、易于控制采样密度和保持曲面细节,且能够生成高质量的简化曲面.An adaptive curvature simplification method for point-sampled model is presented based on geometry images. First, the point-sampled model is represented as geometry images by projecting its spherical polar coordinates onto a plane. Using geometry images, the k-nearest neighbors of each sample point are then determined significantly fast and its surface variation is calculated. Finally, the point set surfaces are re-sampled in combination with the surface variation and simple density control. In addition, the quality of the simplified point set surfaces is evaluated using the error measurement method based on the moving least squares surfaces. Experimental results show that this algorithm is fast, easy to control the simple density and preserve the detail , and can create high-quality surface approximations.

关 键 词：点模型简化 几何图像 曲面变分 移动最小二乘曲面 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术]