Raphael: Realistic 3D data generation and unsupervised learning for enhanced human-robot detection in the real world

Ensuring safety in human–robot interaction requires reliable detection in industrial environments, yet the prohibitive cost of data annotation remains a major obstacle. We present a novel and cost-effective framework for automatic data generation and labeling, enabling unsupervised learning with state-of-the-art super-resolution LiDAR systems. Our method leverages recent advances in mesh reconstruction and animated 3D assets to create realistic synthetic datasets, surpassing existing augmentation strategies in fidelity and scalability. Beyond this, we introduce a second self-supervised stage that iteratively refines the model and bridges the virtual-to-real domain gap. Evaluations on real-world scenarios show substantial gains in performance.

@inproceedings{licaret2025raphael,
  title = {Raphael: Realistic 3D data generation and unsupervised learning for enhanced human-robot detection in the real world},
  author = {Vlad Licaret and Atle Aalerud and Assia Belbachir and Nabil Belbachir and Marius Leordeanu},
  year = {2025},
  booktitle = {Proceedings of the British Machine Vision Conference Workshops (BMVCW)},
}