Abstract:

Aiming at incomprehensive description of 3D model of Silhouette descriptor(SIL) and Density-Based Framework(DBF), this paper proposes a new 3D model retrieval algorithm: Density-based Contour(DBC). It characterizes a 3D object using multivariate probability functions of the object’s 2D contours’ features. Two models can be compared by the similarity of their 2D contours’ probability functions. The new algorithm performs better than SIL on describing contours of the model and it also shows stronger resistance to noise than DBF. The retrieval performance on PSB shows that DBC has a higher retrieval accuracy comparing to other traditional state-of-the-art 3D model retrieval algorithms.

Key words: 3D model retrieval, contour, probability density function, kernel density estimation, feature description, matching invariance

摘要：

针对基于轮廓检索算法(SIL)和基于概率密度检索算法(DBF)对三维模型描述的不足，提出一种基于概率密度和轮廓的三维模型检索算法。利用高斯核密度函数描述模型投影轮廓特征的分布，计算不同模型在同一坐标平面内投影轮廓特征分布的相似度，以得到模型间的相似度。该算法相比SIL对模型轮廓描述更全面，比DBF具有更强的抗干扰性。在PSB数据库上的检索结果表明，与经典算法相比，该算法具有更高的检索准确率。

关键词: 三维模型检索, 轮廓, 概率密度函数, 核密度估计, 特征描述, 匹配不变性

CLC Number: 

• TP181