ABSTRACT

Three-dimensional (3D) computer-aided design (CAD) model reconstruction techniques are used for numerous purposes across various industries, including free-viewpoint video reconstruction, robotic mapping, tomographic reconstruction, 3D object recognition, and reverse engineering. With the development of deep learning techniques, researchers are investigating the reconstruction of 3D CAD models using learning-based methods. Therefore, we proposed a method to effectively reconstruct 3D CAD models containing machining features into 3D voxels through a 3D encoder–decoder network. 3D CAD model datasets were built to train the 3D CAD model reconstruction network. For this purpose, large-scale 3D CAD models containing machining features were generated through parametric modeling and then converted into a 3D voxel format to build the training datasets. The encoder–decoder network was then trained using these training datasets. Finally, the performance of the trained network was evaluated through 3D reconstruction experiments on numerous test parts, which demonstrated a high reconstruction performance with an error rate of approximately 1%.

中文翻译：

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包含机械零件加工特征的基于深度学习的 3D CAD 模型重建数据集和方法

摘要

三维 (3D) 计算机辅助设计 (CAD) 模型重建技术用于各个行业的多种用途，包括自由视点视频重建、机器人绘图、断层重建、3D 对象识别和逆向工程。随着深度学习技术的发展，研究人员正在研究使用基于学习的方法重建 3D CAD 模型。因此，我们提出了一种通过 3D 编码器-解码器网络有效地将包含加工特征的 3D CAD 模型重建为 3D 体素的方法。构建 3D CAD 模型数据集以训练 3D CAD 模型重建网络。为此，通过参数化建模生成包含加工特征的大型 3D CAD 模型，然后将其转换为 3D 体素格式以构建训练数据集。然后使用这些训练数据集训练编码器-解码器网络。最后，通过对大量测试部件进行 3D 重建实验来评估训练后的网络的性能，这证明了高重建性能，错误率约为 1%。