A Synthetic CAD Models Dataset for Deep Learning

3D reconstruction is a significant research topic in the field of Computer-Aided Design (CAD), which is used to recover editable CAD models from original shapes, including point clouds, voxels, meshes, or boundary representations (B-rep). In recent years, deep learning methods (DL) have exhibited significant potential in the field of CAD, but there is currently a lack of large-scale CAD model datasets that support deep learning for parametrized feature-based modeling. Therefore, we synthesized a large-scale dataset containing one million CAD designs to provide labeled CAD model data for supervised training in relevant deep learning tasks.We employed a random synthesis algorithm to generate CAD models and documented the corresponding feature-based modeling processes, including principal primitives (cuboids, prisms, cylinders, cones, and spheres) and detail features (slots, semi-circular slots, through-holes, steps, fillets, chamfers, etc.). For each CAD model in the dataset, we provided four types of data files: a STEP file recording the 3D shape of the CAD model (compliant with ISO 10303-21 and GB/T 16656 standards), a JSON file recording the parametric feature modeling process (compliant with ECMA-404 and ISO/IEC 21778:2017 standards), a B-Rep graph representation file for CAD models (with a .bin suffix, in the format of the open-source graph neural network framework Deep Graph Library), and three-dimensional isometric side views of the CAD model.

三维重建是计算机辅助设计（Computer-Aided Design，CAD）领域的重要研究课题，其核心目标是从原始形态（包括点云、体素、网格或边界表示（Boundary Representation，B-rep））中恢复可编辑的CAD模型。近年来，深度学习（Deep Learning，DL）方法在CAD领域展现出显著的应用潜力，但目前仍缺乏支持基于参数化特征建模的深度学习研究的大规模CAD模型数据集。为此，本研究构建了包含100万个CAD设计方案的大规模数据集，旨在为相关深度学习任务的监督训练提供带标注的CAD模型数据。我们采用随机合成算法生成CAD模型，并记录了对应的基于特征的建模流程，涵盖基础图元（长方体、棱柱、圆柱、圆锥与球体）以及细节特征（槽、半圆形槽、通孔、台阶、圆角、倒角等）。针对数据集中的每个CAD模型，我们提供了四类数据文件：一是记录CAD模型三维形态的STEP文件（符合ISO 10303-21与GB/T 16656标准）；二是记录参数化特征建模流程的JSON文件（符合ECMA-404与ISO/IEC 21778:2017标准）；三是CAD模型的边界表示图文件（后缀为.bin，采用开源图神经网络框架Deep Graph Library的格式）；四是CAD模型的三维等轴测侧视图。

搜集汇总

背景与挑战

背景概述

该数据集是一个大规模合成CAD模型集合，专为深度学习设计，包含100万个CAD模型，提供多种格式的数据文件以支持参数化特征建模的研究。数据集由随机合成算法生成，记录了特征建模过程，适用于计算机辅助设计（CAD）和深度学习领域的研究。