@misc{14036,
  abstract     = {{This paper presents a robotic system developed to enhance
automation in construction workflows through advanced AI
and computer vision technologies. The system integrates
a robotic arm with a 3D point cloud camera and state-
of-the-art 2D pre-trained Deep Learning models, such as
GroundingDINO and SegmentAnything, to detect and seg-
ment construction elements in 3D dynamic, unstructured
environments. By processing point cloud data from the cam-
era and aligning it with real-world coordinates, the system
achieves precise object localization, enabling tasks such as
element handling and assembly. Designed to address chal-
lenges like clutter, occlusion, and variability in construction
sites, this system bridges the gap between controlled labora-
tory conditions and real-world applications. Experimental
evaluations highlight its potential to improve efficiency and
adaptability in construction tasks.}},
  author       = {{Aykin, Yusuf and Sachs, Hans and Gerzen, Nikolai}},
  booktitle    = {{Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)}},
  editor       = {{Sachs, Hans}},
  issn         = {{2413-5844}},
  keywords     = {{Computer Vision, Architecture, Building Construction, Robotics in Construction}},
  publisher    = {{International Association for Automation and Robotics in Construction (IAARC)}},
  title        = {{{Enhancing Robotic Vision through Deep Learning Techniques: From Detection to Construction}}},
  doi          = {{10.22260/isarc2025/0026}},
  year         = {{2025}},
}