3D Simulated Surface Defects Dataset on Car Doors for Deep Learning-Based Industrial Inspection

This dataset provides synthetic samples of surface defects generated on a CAD model of a car door. The defects include bumps and peaks, simulated using Free-Form Deformation (FFD) to ensure geometric realism and adaptability to curved surfaces. Surface acquisition is emulated using a virtual 3D profilometric sensor, incorporating both geometric and sensor noise to closely replicate real-world inspection conditions. All samples are labeled, and the dataset includes depth images, trajectory data, and raw sensor outputs, making it suitable for training and evaluating surface defect detection models in industrial settings. This dataset is associated with the TriPlay repository on GitHub:🔗 GitHub Repository It is also related with the following publication: 📄 Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise (This dataset is also associated with a manuscript currently under review.) 🔑 Key Features High-Quality Synthetic Defects: Includes localized surface deformations (bumps and peaks) modeled with Free-Form Deformation. Virtual Profilometric Scanning: Simulates data acquisition with a 3D profilometer to capture realistic sensor readings. Realistic Sensor Noise: Adds surface and depth distortion to simulate real acquisition conditions. Per-Step Trajectory and Sensor Data: Includes detailed trajectory files and raw outputs per scanning step. Automatically Generated Annotations: Bounding boxes and defect metadata are included for supervised learning.本数据集提供基于汽车车门CAD模型生成的表面缺陷合成样本。缺陷类型涵盖凸起与峰点，采用自由形状变形（Free-Form Deformation，FFD）进行模拟，以保障几何真实性并适配曲面形态。表面采集过程通过虚拟三维轮廓传感器仿真实现，并同时引入几何噪声与传感器噪声，以高度还原真实工业检测场景。 所有样本均带有标注，数据集包含深度图像、轨迹数据与原始传感器输出，可用于工业场景下表面缺陷检测模型的训练与评估。 本数据集关联GitHub平台上的TriPlay仓库：🔗 GitHub Repository 其还与以下学术论文相关： 📄 《基于Perlin噪声模拟散斑效应的激光轮廓仪测量仿真》 （本数据集同时关联一篇处于审稿阶段的手稿。） 🔑 核心特性 - 高质量合成缺陷：包含采用自由形状变形建模的局部表面变形（凸起与峰点） - 虚拟轮廓扫描：模拟三维轮廓传感器的数据采集流程，获取具备真实感的传感器读数 - 真实传感器噪声：添加表面与深度畸变，模拟实际采集场景的噪声特征 - 逐步轨迹与传感器数据：包含详细的扫描轨迹文件与每一步的原始输出 - 自动生成标注：提供可用于监督学习的边界框与缺陷元数据