Paper ID: 2305.05215

Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization

Lukáš Gajdošech, Peter Kravár

Application of neural networks in industrial settings, such as automated factories with bin-picking solutions requires costly production of large labeled data-sets. This paper presents an automatic data generation tool with a procedural model of a cardboard box. We briefly demonstrate the capabilities of the system, its various parameters and empirically prove the usefulness of the generated synthetic data by training a simple neural network. We make sample synthetic data generated by the tool publicly available.

Submitted: May 9, 2023