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

Abstract

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.