多特征融合的非刚性三维模型匹配算法研究

Non-rigid 3D Models Matching with Multi-feature Fusion

特征描述符是影响非刚性三维模型匹配结果的关键因素,而单一特征只能描述三维模型某一方面的信息.为了克服单一特征在模型匹配时的局限性,进一步提高模型匹配的精确度,通过引入信息论中信息熵的概念,结合各单一特征匹配时的结果,计算得到各特征的权值,对多种特征(如热核特征(HKS)、能量分布特征(WKS)和模型表面积特征等)进行融合,作为非刚性三维模型匹配的特征.最后在SHREC’2014提供的标准测试数据集上进行试验,并与单一特征描述符的结果进行对比,验证了多特征融合得到的特征描述符要优于任一单一特征描述符,可以应用于非刚性三维模型检索系统中.

Feature descriptors are the key factors influencing the result of non-rigid 3D model correspondence.But a single feature descriptor only contains one aspect of information of a 3D model.In order to overcome the limitation of single feature and further improve the accuracy of model correspondence,the entropy was introduced to calculate the weight of each single feature according to its correspondence results.The features of HKS（heat kernel signature）,WKS（wave kernel signature）and surface area were fused with these weights.The effectiveness of the approach was evaluated by using the SHREC＇2014 non-rigid 3D human models benchmark.In addition,the results outperform those of any state-of-the-art single feature descriptor,and can be used for nonrigid 3D model retrieval.