基于CSF的SVM多边形细分控制

Naoto Yoshii, S. Saito
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引用次数: 0

摘要

在3D计算机图形中,如果减少模型中多边形的数量不会对视觉产生影响,那么越少越好。在本文中,我们假设一种控制多边形细分层次以降低渲染计算成本的方法,提出了一种使用支持向量机(SVM)根据相邻细节层次之间的可感知性差异从两个细分层次中选择一个细分层次的方法。SVM以绘制过程中得到的四个特征作为输入,进行二值分类,判断多边形是否需要分割。实验结果表明,训练后的支持向量机进行二值分类的准确率为72%。
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Polygon Subdivision Control Using SVM With CSF
In 3D computer graphics, less is better if reducing the number of polygons in a model has no visual impact. In this paper, assuming a method that controls polygon subdivision level to reduce the rendering computational cost, we propose a method that uses a Support Vector Machine (SVM) to choose one from two subdivision levels by the perceptibility difference between the adjacent level of detail. The SVM takes four features obtained in the rendering procedure as input and performs a binary classification to determine whether a polygon should be divided or not. Our experimental results show that the trained SVM performs a binary classification with 72% accuracy.
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