Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection
<|reference_start|>Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection: Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSense3D, which offers a diverse range of scenarios for robust feature learning. Subsequently, we fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions. Experimental results of the Cube R-CNN model on challenging public benchmarks show a remarkable improvement in detection performance, with a mean average precision rising from 0.26 to 12.76 on the TUM Traffic A9 Highway dataset and from 2.09 to 6.60 on the DAIR-V2X-I dataset when performing transfer learning. Code, data, and qualitative video results are available on the project website: https://roadsense3d.github.io.<|reference_end|>
@article{mohamed2024transfer,
    title={Transfer Learning from Simulated to Real Scenes for Monocular 3D Object
  Detection},
    author={Sondos Mohamed, Walter Zimmer, Ross Greer, Ahmed Alaaeldin Ghita,
  Modesto Castrill'on-Santana, Mohan Trivedi, Alois Knoll, Salvatore Mario
  Carta, Mirko Marras},
    journal={arXiv preprint arXiv:2408.15637},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15637},
    primaryClass={cs.CV}
}