面向数控加工的三维人像模型相似度评价技术研究及应用
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摘要
制造业发展的一个重要趋势是不断开发个性化定制产品,以满足快速发展的市场需求。当前,一类与人像有关的个性化定制产品正逐渐形成一个潜在的新兴产业。此产业主要包括两个环节,第一个是基于二维图像重建特定三维人像模型,第二个是把特定三维人像模型用数控机床加工获得人像产品。此产业成败的一个重要问题是人像产品与建模对象之间的相似度问题,其关键在于数控加工前的三维人像模型与建模对象之间的相似度问题。目前针对此产业的三维人像模型相似度评价方法多采用主观评价、专家打分的方式,但是这种方式存在主观性太强、评价结果不稳定的缺点。因此,有必要建立统一的相对客观的相似度评价体系和方法。本文结合个性化定制人像产品开发的研究项目,对面向数控加工的三维人像模型的相似度评价方法进行研究。研究内容主要包括以下几个方面:
首先,通过对面向数控加工的三维人像模型的特点及相似度评价需求的分析,研究了面向数控加工的三维人像模型相似度评价技术体系,该体系包括相似度评价框架结构和相似度评价流程两部分内容,并对相似度评价关键技术进行了分析。
其次,对三维人像模型的配准技术进行研究,该技术包括配准前期准备和模型配准实施两部分内容。在配准前期准备中,对三维人像模型尤其是基于图像的重建模型数据的获取技术进行介绍,对三维人像模型的数据处理技术进行重点研究;在模型配准实施中,针对三维人像重建模型与三维人像基准模型来自不同的坐标系,利用人脸特有的结构形状特征,提出分两步对模型进行配准的方法,即基于人脸特征对应点拾取的初始配准和基于改进ICP算法的精确配准的两步模型配准法。
再次,在模型配准的基础上,研究基于模型误差的三维人像模型相似度计算方法,该方法由模型误差分析和相似度计算模型的构建两个过程构成。模型误差分析主要包括模型误差计算和模型误差显示两部分内容;相似度计算模型的构建主要由模型平均误差的求取和相似度计算函数的建立两个过程构成,重点分析相似度函数需要满足的条件。
最后,把研究成果应用于个性化定制人像产品开发项目中,对本文提出的面向数控加工的三维人像模型相似度评价方法进行验证和初步应用。
An important development trend of manufacturing is to develop personalized customized products continually to meet growing market demands. Currently, a kind of personalized customized products with human portraits is gradually forming a potentially emerging industry. This industry mainly contains two parts, the first one is reconstructing three-dimensional (3D) portrait model for that person based on 2D image, the second one is the 3D portrait model was processed with NC machine to obtain 3D personalized customized products with human portraits. A vital point in this industry is the similarity between the product and the modeling object, the key of which is the similarity between the 3D portrait model before NC machining and modeling object. At present, the method of subjective evaluation and expert scoring is used widely, but this method owns some disadvantages, for example, the subjectivity is too strong, and the evaluation results are not stable. Consequently, it’s necessary to establish uniform and relatively objective similarity evaluation system. With the support of personalized customized products with human portraits developing project, the similarity evaluation method of 3D portrait model for NC machining is studied. The main study includes the following contents:
To begin with, based on the analysis of 3D portrait model’s characteristics for NC machining and the need for the similarity evaluation, the similarity evaluation system for 3D portrait model for NC machining is studied, which is composed of two parts, namely, similarity evaluation frame and similarity evaluation process. In addition, the key techniques of similarity evaluation are analyzed.
Then, the 3D portrait model registration technology is studied, which is composed by the prepared work before registration and model registration implementation work. Among the prepared work before registration, the acquirement techniques of 3D portrait model, especially the reconstructed 3D portrait model based on images are introduced, and data processing techniques are analyzed in detail. Among the model registration implementation work, in view of 3D portrait reconstructed model and 3D portrait standard model from two different coordinate systems, the model registration technology containing two stages is studied based on the unique structure shape characteristic of face, which are the initial registration based on picking face feature corresponding points and the accurate registration based on the modified ICP Algorithm.
And then, on the basis of model registration, a similarity evaluation method based on model error for 3D portrait model is studied. The method is composed by the process of model error analysis and the process of building similarity calculation model. Model error analysis mainly includes two parts, which are model error calculation and model error display. The build of similarity calculation model mainly contains the process of the calculating the model average error and the process of establishing the similarity computing function, and the conditions that similarity functions need to meet are analyzed in detail.
Finally, the research results are applied in personalized customized products with human portraits developing project, the similarity evaluation method of 3D portrait model for NC machining proposed in the dissertation is validated the practicality and efficacy.
首先,通过对面向数控加工的三维人像模型的特点及相似度评价需求的分析,研究了面向数控加工的三维人像模型相似度评价技术体系,该体系包括相似度评价框架结构和相似度评价流程两部分内容,并对相似度评价关键技术进行了分析。
其次,对三维人像模型的配准技术进行研究,该技术包括配准前期准备和模型配准实施两部分内容。在配准前期准备中,对三维人像模型尤其是基于图像的重建模型数据的获取技术进行介绍,对三维人像模型的数据处理技术进行重点研究;在模型配准实施中,针对三维人像重建模型与三维人像基准模型来自不同的坐标系,利用人脸特有的结构形状特征,提出分两步对模型进行配准的方法,即基于人脸特征对应点拾取的初始配准和基于改进ICP算法的精确配准的两步模型配准法。
再次,在模型配准的基础上,研究基于模型误差的三维人像模型相似度计算方法,该方法由模型误差分析和相似度计算模型的构建两个过程构成。模型误差分析主要包括模型误差计算和模型误差显示两部分内容;相似度计算模型的构建主要由模型平均误差的求取和相似度计算函数的建立两个过程构成,重点分析相似度函数需要满足的条件。
最后,把研究成果应用于个性化定制人像产品开发项目中,对本文提出的面向数控加工的三维人像模型相似度评价方法进行验证和初步应用。
An important development trend of manufacturing is to develop personalized customized products continually to meet growing market demands. Currently, a kind of personalized customized products with human portraits is gradually forming a potentially emerging industry. This industry mainly contains two parts, the first one is reconstructing three-dimensional (3D) portrait model for that person based on 2D image, the second one is the 3D portrait model was processed with NC machine to obtain 3D personalized customized products with human portraits. A vital point in this industry is the similarity between the product and the modeling object, the key of which is the similarity between the 3D portrait model before NC machining and modeling object. At present, the method of subjective evaluation and expert scoring is used widely, but this method owns some disadvantages, for example, the subjectivity is too strong, and the evaluation results are not stable. Consequently, it’s necessary to establish uniform and relatively objective similarity evaluation system. With the support of personalized customized products with human portraits developing project, the similarity evaluation method of 3D portrait model for NC machining is studied. The main study includes the following contents:
To begin with, based on the analysis of 3D portrait model’s characteristics for NC machining and the need for the similarity evaluation, the similarity evaluation system for 3D portrait model for NC machining is studied, which is composed of two parts, namely, similarity evaluation frame and similarity evaluation process. In addition, the key techniques of similarity evaluation are analyzed.
Then, the 3D portrait model registration technology is studied, which is composed by the prepared work before registration and model registration implementation work. Among the prepared work before registration, the acquirement techniques of 3D portrait model, especially the reconstructed 3D portrait model based on images are introduced, and data processing techniques are analyzed in detail. Among the model registration implementation work, in view of 3D portrait reconstructed model and 3D portrait standard model from two different coordinate systems, the model registration technology containing two stages is studied based on the unique structure shape characteristic of face, which are the initial registration based on picking face feature corresponding points and the accurate registration based on the modified ICP Algorithm.
And then, on the basis of model registration, a similarity evaluation method based on model error for 3D portrait model is studied. The method is composed by the process of model error analysis and the process of building similarity calculation model. Model error analysis mainly includes two parts, which are model error calculation and model error display. The build of similarity calculation model mainly contains the process of the calculating the model average error and the process of establishing the similarity computing function, and the conditions that similarity functions need to meet are analyzed in detail.
Finally, the research results are applied in personalized customized products with human portraits developing project, the similarity evaluation method of 3D portrait model for NC machining proposed in the dissertation is validated the practicality and efficacy.
引文
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[6] S. Romdhani, V. Blanz, T.Vetter. Face identification by fitting a 3D morphable model using linear shape and texture error functions[C]. Proceedings of the 7th European Conference. Computer Vision, London: Springer-Verlag ,2002:3-19.
[7] Hei-Sheung Koo,Kin-Man Lam. Recovering the 3D shape and poses of face images based on the similarity transform[J]. Pattern Recognition Letters,2008,29(6):712–723.
[8]龚勋,王国胤.基于特征点的三维人脸形变模型[J].软件学报, 2009,20(3):724–733.
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[10]梁勇,程红,孙文邦等.图像配准方法研究[J].影像技术,2010,(4):15-17.
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[14] Besl P J,Mckay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1992, 14(2):239-256.
[15]张政.点云数据配准算法研究[D].山东:山东大学,2008.
[16] Z.Zhang. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision, 1994, 13(2):119–152.
[17] D.A. Simon, M. Hebert, and T. Kanade. Real-time 3-D pose estimation using a high-speed range sensor[J]. IEEE International Conference on Robotics and Automation, May 1994, 8-13:2235–2241.
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[19] Natasha Gelfand,Leslie Ikemoto,Szymon Rusinkiewicz,et al. Geometrically Stable Sampling for the ICP Algorithm[EB/OL]. http://scholar.google.com.hk. 2011-04-18.
[20] Niloy J. Mitra , Natasha Gelfand , Helmut Pottmann ,et al. Registration of Point Cloud Data from a Geometric Optimization Perspective[C]. Proceedings of the 2004 Eurographics / ACM SIGGRAPH Symposium on Geometry Processing, 2004.
[21]罗先波,钟约先,李仁举.三维扫描系统中的数据配准技术[J].清华大学学报自然科学版,2004,44(8):1104-1106.
[22]王磊,邢渊.反向工程中数据点云的拼合[J].模具技术.2004,(1):47-49,56.
[23]朱延娟,周来水,张丽艳.散乱点云数据配准算法[J].计算机辅助设计与图形学学报,2006,18(4):476-481.
[24]戴静兰,陈志杨,叶修梓. ICP算法在点云配准中的应用[J].中国图象图形学报,2007,12(3):517-521.
[25]严思杰,周云飞,彭芳瑜等.大型复杂曲面零件加工余量均布优化问题研究[J].华中科技大学学报(自然科学版),2002,30(10):35-37.
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