三维模型特征提取技术研究
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摘要
随着三维模型数量的迅速增加,三维模型检索技术研究的重要性日益凸显。针对大规模三维模型库快速检索需求,本文围绕三维模型检索技术中的低层特征提取、高层语义特征标注、相关反馈等关键技术展开深入研究,研究具有重要的理论意义和实际应用价值。
研究工作主要进展包括如下方面：
(1)提出了一种三维模型线形骨架提取方法。先计算离散体素表示的三维模型的向量场,再利用结果向量场的关键点、关键曲线等拓朴特征,抽取多层次线形骨架,借助改进的推土机距离EMD方法以度量线形骨架之间的相似性。基于该算法提取的线形骨架可实现模型分类、全局匹配和局部匹配,有效提升了检索效果。
(2)给出了基于局部特征概率密度估计的三维模型特征提取的体系框架。针对三维表面局部几何特征集,利用核密度估计方法估计选定目标点的特定局部特征密度,构成特征向量,用以描述三维模型。抽取三维网格模型的单元特征及多个单元特征组合而成的多元特征支持实现三维模型检索,实验验证其检索性能优于基于统计的直方图特征提取方法。
(3)提出了语义力相关反馈(SFRF)和纯语义相关反馈(PSRF)两种相关反馈新算法。将三维模型看作是彼此相互作用的带电粒子,其所带电量的正负、大小取决于用户对模型之间语义相似度的打分评判；依据这些带电粒子之间的相互作用,不断重新调整三维模型的点向量在其特征空间中的位置,构成语义聚类。借助这两种方法实现三维模型检索,语义力相关反馈(SFRF)方法的性能远胜于特征空间变形算法SpaceWarping,纯语义相关反馈(PSRF)方法也表现出很好的检索性能。
(4)提出一个三维模型半自动语义标注框架。基于模型的内容信息选取训练样例；利用系统获取的模型标注知识,借助模糊神经控制器集合估计数据库中每一个模型所属类别；将提出的两种相关反馈方法集成到三维模型语义标注框架之中以支持和提高语义标注的性能。实验表明该框架从本质上提高了语义标注的进程,能有效地支撑实现三维模型数据库的自动语义标注过程。
本研究得到国家自然科学基金《文物三维模型的语义标注与本体检索技术研究(60873094)》的支持。
With rapid growth in the amount of 3D models, the research on 3D model retrieval technology is becoming increasingly important. To meet the requirements of retrieval in 3D model massive database, this dissertation investigates the key techniques of the 3D model retrieval technology including low-level feature extraction, high-level semantic feature annotation, similarity measure and relevance feedback and so on. The research has important academic significance and application value.
The main works are listed as follows:
(1) A novel algorithm to extract the curve-skeleton of 3d model is introduced. The algorithm firstly calculates the 3D vector fields of models represented by discrete unit, and then extracts the hierarchical curve-skeleton based on topological features of the critical curve and critical points of the vector fields. The similarity among 3D curve-skeletons is measured by using an improved Earth Mover's Distance (EMD) algorithm. The curve-skeleton extracted with this novel algorithm can be used to categorize models and implement global matching and partial matching.
(2) A new architecture for extraction of 3D model features using probabilistic density estimation of local surface features is proposed. With the set of 3D local geometrical features, the local feature density of a chosen target point is evaluated using probabilistic density estimation methods. The 3D model can be described using the feature vector comprised of all local feature density values. The single-variate and multi-variate descriptors of 3D mesh model supports for the implementation of 3D model retrieval. The results show that the retrieval performance the method is better than that of the statistical feature extraction methods.
(3) Two methods implementing Semantic Force Relevant Feedback(SFRF) and Pure Semantic Relevance Feedback(PSRF) are presented.3D models can be regarded as interactive charged particles. The quantity of electricity depends on the users'judge against the semantic similarity among the models. The charged elements apply forces to each other in a way that semantically clusters are formed and the retrieval quality is enhanced. The two methods were used to implement 3D model retrieval. The outcome shows that SFRF algorithm outperformed the Feature Space Warping and the PSFR algorithm, also illustrated very good performance on the performed experiments.
(4) A semiautomatic semantic annotation scheme for 3D models is proposed. The training examples are selected based on the content of a 3D model. A ncurofuzzy controller set is used to estimate the attributes (categories) of each database model using knowledge obtained from manual annotations of objects suggested by the system. Additionally, two relevance feedback methods were modified and integrated for supporting and enhancing the annotation procedure. The proposed framework induces a substantial acceleration of the annotation process. The experiments results show that the proposed method is superior in terms of efficiency for the automatic, semantic annotation of 3D model databases.
This research work was supported by Natural Science Foundation project of China "research on the key technology of semantic annotation and ontology based retrieval for 3D cultural model (No.60873094)".
研究工作主要进展包括如下方面：
(1)提出了一种三维模型线形骨架提取方法。先计算离散体素表示的三维模型的向量场,再利用结果向量场的关键点、关键曲线等拓朴特征,抽取多层次线形骨架,借助改进的推土机距离EMD方法以度量线形骨架之间的相似性。基于该算法提取的线形骨架可实现模型分类、全局匹配和局部匹配,有效提升了检索效果。
(2)给出了基于局部特征概率密度估计的三维模型特征提取的体系框架。针对三维表面局部几何特征集,利用核密度估计方法估计选定目标点的特定局部特征密度,构成特征向量,用以描述三维模型。抽取三维网格模型的单元特征及多个单元特征组合而成的多元特征支持实现三维模型检索,实验验证其检索性能优于基于统计的直方图特征提取方法。
(3)提出了语义力相关反馈(SFRF)和纯语义相关反馈(PSRF)两种相关反馈新算法。将三维模型看作是彼此相互作用的带电粒子,其所带电量的正负、大小取决于用户对模型之间语义相似度的打分评判；依据这些带电粒子之间的相互作用,不断重新调整三维模型的点向量在其特征空间中的位置,构成语义聚类。借助这两种方法实现三维模型检索,语义力相关反馈(SFRF)方法的性能远胜于特征空间变形算法SpaceWarping,纯语义相关反馈(PSRF)方法也表现出很好的检索性能。
(4)提出一个三维模型半自动语义标注框架。基于模型的内容信息选取训练样例；利用系统获取的模型标注知识,借助模糊神经控制器集合估计数据库中每一个模型所属类别；将提出的两种相关反馈方法集成到三维模型语义标注框架之中以支持和提高语义标注的性能。实验表明该框架从本质上提高了语义标注的进程,能有效地支撑实现三维模型数据库的自动语义标注过程。
本研究得到国家自然科学基金《文物三维模型的语义标注与本体检索技术研究(60873094)》的支持。
With rapid growth in the amount of 3D models, the research on 3D model retrieval technology is becoming increasingly important. To meet the requirements of retrieval in 3D model massive database, this dissertation investigates the key techniques of the 3D model retrieval technology including low-level feature extraction, high-level semantic feature annotation, similarity measure and relevance feedback and so on. The research has important academic significance and application value.
The main works are listed as follows:
(1) A novel algorithm to extract the curve-skeleton of 3d model is introduced. The algorithm firstly calculates the 3D vector fields of models represented by discrete unit, and then extracts the hierarchical curve-skeleton based on topological features of the critical curve and critical points of the vector fields. The similarity among 3D curve-skeletons is measured by using an improved Earth Mover's Distance (EMD) algorithm. The curve-skeleton extracted with this novel algorithm can be used to categorize models and implement global matching and partial matching.
(2) A new architecture for extraction of 3D model features using probabilistic density estimation of local surface features is proposed. With the set of 3D local geometrical features, the local feature density of a chosen target point is evaluated using probabilistic density estimation methods. The 3D model can be described using the feature vector comprised of all local feature density values. The single-variate and multi-variate descriptors of 3D mesh model supports for the implementation of 3D model retrieval. The results show that the retrieval performance the method is better than that of the statistical feature extraction methods.
(3) Two methods implementing Semantic Force Relevant Feedback(SFRF) and Pure Semantic Relevance Feedback(PSRF) are presented.3D models can be regarded as interactive charged particles. The quantity of electricity depends on the users'judge against the semantic similarity among the models. The charged elements apply forces to each other in a way that semantically clusters are formed and the retrieval quality is enhanced. The two methods were used to implement 3D model retrieval. The outcome shows that SFRF algorithm outperformed the Feature Space Warping and the PSFR algorithm, also illustrated very good performance on the performed experiments.
(4) A semiautomatic semantic annotation scheme for 3D models is proposed. The training examples are selected based on the content of a 3D model. A ncurofuzzy controller set is used to estimate the attributes (categories) of each database model using knowledge obtained from manual annotations of objects suggested by the system. Additionally, two relevance feedback methods were modified and integrated for supporting and enhancing the annotation procedure. The proposed framework induces a substantial acceleration of the annotation process. The experiments results show that the proposed method is superior in terms of efficiency for the automatic, semantic annotation of 3D model databases.
This research work was supported by Natural Science Foundation project of China "research on the key technology of semantic annotation and ontology based retrieval for 3D cultural model (No.60873094)".
引文
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