# Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization

arXiv: https://arxiv.org/abs/2305.05215 · HTML: https://mlsanity.com/paper/2305.05215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 4 of 4)

- [Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking](https://mlsanity.com/paper/2204.07049/) (2022)
- [Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture](https://mlsanity.com/paper/2104.12564/) (2021)
- [Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans](https://mlsanity.com/paper/2112.09598/) (2021)
- [SynPick: A Dataset for Dynamic Bin Picking Scene Understanding](https://mlsanity.com/paper/2107.04852/) (2021)

## 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.