Improving the rapidity of 3D reconstruction is vital for time-critical construction tasks such as progress monitoring and hazard detection, but the majority of construction studies in this area have focused on improving its quality. We applied a Direct Sparse Odometry with Loop Closure (LDSO)-based 3D reconstruction method, improving the existing algorithm and tuning its hyper-parameter settings, to achieve both near real-time operation and quality 3D point cloud simultaneously. When validated using a benchmark dataset, the proposed method showed notable improvement in 3D point cloud density, as well as loop closure robustness, compared to the original LDSO. In addition, we conducted a real field test to validate the tuned LDSO’s accuracy and speed at both object and site scales, where we demonstrated our method’s near real-time operation and capability to produce a quality 3D point cloud comparable to that of the existing method. The proposed method improves the accessibility of the 3D reconstruction technique, which in turn helps construction professionals monitor their jobsite safety and progress in a more efficient manner.

Deploying construction robots alongside workers presents the risk of unwanted forcible contact—a critical safety concern. To address a semantic digital twin where such contact-driven hazards can be monitored accurately, the authors present a single-shot deep neural network (DNN) model that can perform proximity and relationship detections simultaneously. Given that workers and construction robots must sometimes collaborate in close proximity, their relationship must be considered, along with proximity, before concluding an event is a hazard. To address this issue, we leveraged a unique two-in-one DNN architecture called Pixel2Graph (i.e., object + relationship detections). The potential of this DNN architecture for relationship detection was confirmed by follow-up testing using real-site images, achieving 90.63% recall@5 when object bounding boxes and classes were given. When integrated with existing proximity monitoring methods, single-shot visual relationship detection will enable the accurate identification of contact-driven hazards in a digital twin platform, an essential step in realizing sustainable and safe collaboration between workers and robots.