3D CAD models play an important role in large-scale engineering projects. The increasing demand for highly-detailed datasets presents a challenge for efficient storage, transmission, and rendering. To reduce dataset size, 3D shape matching techniques have been proposed to find repeated triangle meshes, but are strongly dependent on surface triangulation. Meanwhile, existing shape registration techniques are not well suited for the 3D CAD domain. In this paper, we present a deep learning-based framework that uses point clouds to identify repeated instances of triangle meshes (a single 3D CAD model component) overcoming the limitations of previous work and guaranteeing an upper bound on any geometric errors. The framework combines PointNet++ for shape classification with a registration procedure based on Principal Component Analysis and the Adam optimizer. The resulting affine transformation can be used to efficiently instantiate repeated CAD geometries. Using the proposed framework, we were able to reduce a real-world 3D CAD model to 2,61% of its original size, while preserving its geometric accuracy and improving rendering performance.

3D CAD 模型在大型工程项目中发挥着重要作用。对高度详细数据集日益增长的需求对高效存储、传输和渲染提出了挑战。为了减少数据集大小，已经提出了 3D 形状匹配技术来查找重复的三角形网格，但强烈依赖于表面三角剖分。同时，现有的形状配准技术不太适合 3D CAD 领域。在本文中，我们提出了一个基于深度学习的框架，该框架使用点云来识别三角形网格（单个 3D CAD 模型组件）的重复实例，克服了以前工作的局限性并保证了任何几何错误的上限。该框架将用于形状分类的 PointNet++ 与基于主成分分析和 Adam 优化器的注册程序相结合。由此产生的仿射变换可用于有效地实例化重复的 CAD 几何图形。使用建议的框架，我们能够将真实世界的 3D CAD 模型缩小到其原始尺寸的 2.61%，同时保持其几何精度并提高渲染性能。