融合双尺度特征的草图化三维零件库检索方法研究
Research on Retrieval Method for Sketching 3D Parts Library by Fusing Dual Scale Features
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摘要: 基于草图的三维模型检索是机械零件库中三维模型检索的重要途经. 草图和三维模型之间存在着巨大的模态差异, 针对三维模型投影出的多个视图的信息冗余, 零件视图提取的特征的表征能力弱的问题, 为了提高基于草图的零件三维模型检索的效果, 通过融合零件模型的局部和全局的双尺度特征, 提出一种草图化三维机械零件库检索方法. 首先提出基于图像熵的三维模型投影视图表达方法, 减少多个视图之间的冗余性; 然后提出面向零件视图边缘轮廓提取的mechanical sketch模型, 深度提取零件的轮廓草图, 以逼近视图和草图的相似性; 最后构建视觉词袋模型和改进的多视图卷积神经网络模型来分别提取轮廓草图的局部特征和全局特征, 通过融合双尺度特征, 采用欧几里得距离进行基于草图的零件三维模型匹配. 在扩充的ESB数据集上的实验结果表明, 所提方法在准确率和召回率等系列评价指标上, 相比现有的6种方法, 有了较为明显的提高, 验证了所提方法的可行性和有效性.Abstract: Sketch-based 3D model retrieval is an important way to retrieve 3D models in mechanical part library. There are huge modal differences between sketches and 3D models. Aiming at the information redundancy of multiple views projected by the 3D model, the weak representation ability of the features extracted from the part views, in order to improve the effect of sketch-based 3D model retrieval, a sketch-based 3D mechanical part Library retrieval method is proposed to fuse the local and global dual-scale features of the part model. Firstly, a projection view representation method of 3D model based on image entropy is proposed to reduce the redundancy among multiple views. Then, a mechanical sketch model for edge contour extraction of part view is proposed, and the contour sketch of the part is extracted in depth to approximate the similarity between the view and the sketch. Finally, a visual word bag model and an improved multi-view convolutional neural network model is constructed to extract the local and global features of the contour sketch respectively. By fusing the dual -scale features, Euclidean distance is used to match the 3D model of the part based on the sketch. The experimental results on ESB datasets show that the proposed method is significantly improved in a series of evaluation indicators such as accuracy and recall rate compared with the existing 6 common methods, which verifies the feasibility and effectiveness of the proposed method.