Steven Moonen, Nick Michiels, Abdellatif Bey-Temsamani, bram vanherle, joris dehoog and taoufik bourgana
Adv. Artif. Intell. Mach. Learn., 3 (2):977-995
Steven Moonen : UHasselt
Nick Michiels : uhasselt
Abdellatif Bey-Temsamani : Flanders Make
bram vanherle : UHasselt
joris dehoog : Flanders make
taoufik bourgana : Flanders make
Article History: Received on: 17-Feb-23, Accepted on: 28-Mar-23, Published on: 07-Apr-23
Corresponding Author: Steven Moonen
Citation: Steven Moonen (2023). CAD2Render: A Synthetic data generator for training object detection and pose estimation models in industrial environments.. Adv. Artif. Intell. Mach. Learn., 3 (2 ):977-995
Computer vision systems become more wide spread in the manufacturing industry for automating tasks. As these vision systems use more and more machine learning opposed to the classic vision algorithms, streamlining the process of creating the training datasets become more important. Creating large labeled datasets is a tedious and time consuming process that makes it expensive. Especially in a low-volume high-variance manufacturing environment.
To reduce the costs of creating training datasets we introduce CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render streamlines the process of creating highly customizable synthetic datasets with a modular design for a wide range of variation settings.
We validate our toolkit by showcasing the performance of AI vision models trained purely with synthetic data. The performance is tested on object detection and pose estimation problems in a variate of industrial relevant use cases.