MVCNN++: CAD model shape classification and retrieval using multi-view convolutional neural networks

Deep neural networks have shown promising success towards the classification and retrieval tasks for images and text data. While there have been several implementations of deep networks in the area of computer graphics, these algorithms do not translate easily across different datasets, especially for shapes used in product design and manufacturing domain. Unlike datasets used in the 3D shape classification and retrieval in the computer graphics domain, engineering level description of 3D models do not yield themselves to neat distinct classes. The current study looks at an improved form of the 3D shape deep learning algorithm for classification and retrieval through the use of techniques such as relaxed classification, use of prime angled camera angles for capturing feature detail and transfer learning for reducing the amount of data and processing time needed to train shape recognition algorithms. The proposed algorithm (MVCNN++) builds on top of multi-view convolutional neural networ...

深度神经网络在图像与文本数据的分类及检索任务中已展现出令人瞩目的成效。尽管计算机图形学领域已有多款深度网络的落地实现，但此类算法难以在不同数据集间通用适配，尤其针对产品设计与制造领域所使用的三维形状数据时，适配性短板更为显著。与计算机图形学领域中用于三维形状分类与检索的常规数据集不同，工程级三维模型的描述文本难以被划归为清晰明确的类别。本研究针对三维形状的分类与检索任务，提出一种改进型三维形状深度学习算法，通过引入松弛分类技术、采用最优视角相机拍摄以捕捉特征细节，以及运用迁移学习以缩减形状识别算法训练所需的数据量与处理时长。所提出的算法（MVCNN++）以多视图卷积神经网络（Multi-view Convolutional Neural Network）为基础进行优化拓展……