This paper addresses the challenge of geometric quality assurance in
manufacturing, particularly when human assessment is required. It proposes
using Blender, an open-source simulation tool, to create synthetic datasets for
machine learning (ML) models. The process involves translating expert
information into shape key parameters to simulate deformations, generating
images for both deformed and non-deformed objects. The study explores the
impact of discrepancies between real and simulated environments on ML model
performance and investigates the effect of different simulation backgrounds on
model sensitivity. Additionally, the study aims to enhance the model's
robustness to camera positioning by generating datasets with a variety of
randomized viewpoints. The entire process, from data synthesis to model
training and testing, is implemented using a Python API interfacing with
Blender. An experiment with a soda can object validates the accuracy of the
proposed pipeline.

这篇论文介绍了在制造业中几何质量保证的挑战，特别是在需要人工评估时。它提出使用 Blender（一种开源模拟工具）为机器学习模型创建合成数据集，通过将专家信息转化为形状关键参数以模拟变形，生成包括变形和未变形物体的图像。该研究探讨了真实环境和模拟环境之间的差异对机器学习模型性能的影响，并调查了不同模拟背景对模型敏感性的影响。此外，该研究旨在通过生成具有多种随机视点的数据集来增强模型对相机定位的稳健性。整个过程，从数据合成到模型训练和测试，都使用与 Blender 接口的 Python API 进行实现。通过对一个苏打罐对象的实验，验证了所提出的流程的准确性。