Automatic marking method composed of three-dimensional models

Abstract

The invention discloses an automatic marking method composed of three-dimensional models, which comprises the following steps that joint segmentation is performed on an input similar three-dimensional model set, so as to obtain an initial component cluster; the joint segmentation process comprises the following steps that patch level feature extraction is performed on each three-dimensional model in the three-dimensional model set, so as to obtain patch level features of the three-dimensional model, and the patch level features are utilized to perform single-model pre-segmentation on each three-dimensional model in the three-dimensional model set, thereby obtaining an initial component set of all of the three-dimensional models in the three-dimensional model set; component level feature extraction is performed on initial components, so as to obtain component level features of the initial components, and a spectral clustering method is adopted on the basis to cluster the initial components, thereby obtaining a plurality of initial component clusters; and a statistical model is constructed for each initial component cluster and used for patch segmentation of an object model to be marked by adopting a graph cutting optimization method, and marking information of the object model is obtained.

Description

technical field [0001] The invention relates to a processing method for shape analysis, which belongs to the technical field of computer graphics, in particular to an automatic labeling method based on joint segmentation of three-dimensional models. Background technique [0002] Segmenting a 3D model and obtaining the annotations of its components is the basis for shape understanding and processing. Many tasks in the fields of geometric modeling, manufacturing, 3D model animation, and texture rely on component segmentation, and many of these problems require further analysis of segmentation. Parts are annotated, that is, identified as an instance of a known part type. In most cases of these applications, the segmentation and labeling of the input 3D model is performed manually. For example, in the application of human mesh texture synthesis, it is necessary to manually identify the part with "arm" texture or the part with "leg" texture in the mesh; in addition, some applica...

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Problems solved by technology

[0003] Although a lot of work has been carried out on automatic image annotation, such as literature 1: Bao Hong, Xu Guangmei, Feng Songhe, Xu De. Research progress in automatic image annotation technology. Computer Science, 2011,38(7):35-40. However, the 3D model Most of the work in this field is only researched on the overall labeling of 3D models, such as literature 2: Tian Feng, Shen Xukun, Liu Xianmei, Zhou Kai, Du Ruishan. A method for automatic semantic labeling of 3D models based on weak labels, Journal of System Simulation, 2012, 24 (9): 1873-1876, 1881, but did not involve the automatic labeling of 3D model components; in addition, the model segmentation that is inseparable from the 3D model component labeling, such as literature 3: Chen X., Golovinskiy A., Funkhouser T.A Benchmark for3D Mesh Segmentation. ACM Transactions on Graphics, 2009, 28(3). The above is still an open research problem. So far, almost all model segmentation methods only consider the geometric features of the parts themselves, regardless of the category information, and it is difficult to To meet the subsequent labeling requirements of model components, literature 4: Kalogerakis E., Hertzmann A., Singh K.. Learning3D mesh segmentation and labeling. ACM Transactions on Graphics, 2010, 29 (4) Article No.102. First proposed a data Driven model segmentation and labeling method, they learn the model set of manual segmentation and labeling, and express the model part labeling problem as a conditional random field optimization problem, so as to realize the segmentation and labeling of unknown models, and verify the knowledge of manual labeling Facilitating the segmentation of 3D models, however, the method relies on a large collection of manually annotated 3D models

[0004] On the other hand, Document 5: Golovinskiy A., Funkhouser T. Consistent segmentation of 3D models. Computers and Graphics (Shape Modeling International09) 2009, 33(3): 262-269., Document 6: Xu Kai. Semantic-Driven 3D Shape Analysis and modeling. [D] Graduate School of National University of Defense Technology. 2011. Considering that the 3D model of the same object contains more semantic information than a single model, it is proposed to analyze the same model set to obtain the consistency of multiple models A joint segmentation method for segmentation, but it does not consider the automatic labeling of unknown 3D models

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