Is AI superior to multimodal 3D sensor technology for transparent objects? |
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| Christina Junger, Benjamin Simon, Gunther Notni |
- Abstract:
- Transparent objects challenge 3D perception in robotics, especially in navigation and human-robot collaboration. Conventional 3D sensors in the visible or near-infrared spectrum often fail to detect transparent materials due to their optical properties. Collecting real-world datasets for deep learning is difficult and time-consuming because ground truth acquisition requires complex preparation. Multimodal 3D sensors like thermal 3D cameras can automate dataset creation but are costly and need restrictive safety setups. Combining standard 3D sensors or RGB cameras with zero-shot deep learning models offers a promising alternative, enabling recognition of unseen transparent objects without task-specific training. However, the accuracy and feasibility of such zero-shot methods for transparent object perception remain underexplored. This paper presents an initial investigation into their potential and limitations.
- Download:
- IMEKO-TC2-2025-014.pdf
- DOI:
- 10.21014/tc2-2025.014
- Event details
- IMEKO TC:
- TC2
- Event name:
- IMEKO TC2 PhotoMet 2025
- Title:
2025 IMEKO TC2 International Symposium on Modern Photonic Metrology
PhotoMet 2025 - Shaping the Future of Photonic Metrology
- Place:
- Modena, ITALY
- Time:
- 01 September 2025 - 03 September 2025