用于三维形状匹配和检索的深度学习形状描述符

J. Xie, Yi Fang, Fan Zhu, E. Wong
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引用次数: 134

摘要

三维模型复杂的几何结构变化给三维形状匹配和检索带来了很大的挑战。在本文中,我们提出了一种高级形状特征学习方案,通过一种新的判别深度自编码器提取对变形不敏感的特征。首先,开发了一个多尺度形状分布作为自编码器的输入。然后,通过对隐藏层神经元施加Fisher判别准则,我们开发了一种用于形状特征学习的新型判别深度自编码器。最后,将来自多个判别式自编码器的隐藏层中的神经元连接起来形成一个形状描述符,用于三维形状匹配和检索。在包含具有较大几何变化的3D模型的代表性数据集(即Mcgill和SHREC'10 ShapeGoogle数据集)上对所提出的方法进行了评估。在基准数据集上的实验结果证明了该方法对三维形状匹配和检索的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval
Complex geometric structural variations of 3D model usually pose great challenges in 3D shape matching and retrieval. In this paper, we propose a high-level shape feature learning scheme to extract features that are insensitive to deformations via a novel discriminative deep auto-encoder. First, a multiscale shape distribution is developed for use as input to the auto-encoder. Then, by imposing the Fisher discrimination criterion on the neurons in the hidden layer, we developed a novel discriminative deep auto-encoder for shape feature learning. Finally, the neurons in the hidden layers from multiple discriminative auto-encoders are concatenated to form a shape descriptor for 3D shape matching and retrieval. The proposed method is evaluated on the representative datasets that contain 3D models with large geometric variations, i.e., Mcgill and SHREC'10 ShapeGoogle datasets. Experimental results on the benchmark datasets demonstrate the effectiveness of the proposed method for 3D shape matching and retrieval.
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