Chunmei Hu, Xiangpei Huang, Guofang Xia, Xi Liu, Xinjian Ma
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引用次数: 0
Abstract
In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.
近年来,随着文物三维数字化的发展,大多数文化遗址都包含了大量精细的文物三维数据,尤其是彩绘文物等复杂几何体。目前,如何从精细的彩绘文物三维彩色模型中自动提取表面损伤信息,并避免使用传统方法缩减尺寸造成的精度损失,是一个亟待解决的问题。针对上述问题,本文提出了一种基于三维精细数据的文物表面脱落病害自动高精度提取方法。首先,本文基于三维引擎 OpenSceneGraph 设计了一个二维和三维集成数据转换模型,实现了三维彩色模型纹理和二维图像之间的相互转换。其次,本文提出了一种具有自适应 k 值的简单线性迭代聚类分割算法,解决了超像素 k 值的设置问题,提高了图像分割的准确性。最后,通过二维和三维集成模型,对疾病进行统计分析,并在三维模型上进行标注。实验表明,对于表面复杂的彩绘塑料物体,本文提出的基于三维精细模型的病害提取方法与目前流行的正射影像提取方法相比,几何精度有所提高,病害调查更加全面。与目前商业软件中的三维人工提取方法相比,该方法在保证提取精度的同时,大大提高了病害提取的效率。本文的研究方法激活了现有众多文保单位的三维精细数据,将传统的二维数据挖掘分析转化为三维,在精度和效率上更符合数据的科学利用,具有一定的科研价值、引领价值和现实意义。
期刊介绍:
Heritage Science is an open access journal publishing original peer-reviewed research covering:
Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance.
Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies.
Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers.
Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance.
Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance.
Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects.
Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above.
Description of novel technologies that can assist in the understanding of cultural heritage.