Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 4 of its 6 references that are in the map. First posted on arXiv 9 May 2023.
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4 of 6 references.
References · 4
- 2022Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Pickingcited by 20
- 2021Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecturecited by 7
- 2021Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scanscited by 4
- 2021SynPick: A Dataset for Dynamic Bin Picking Scene Understandingcited by 3
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