However, research in Digital Twins for the built environment is still in its nascent stages and there is a need to understand the advances in the underlying enabling technologies and establish a convergent context for ongoing and future research. Digital Twins aim to achieve synchronization of the real world with a virtual platform for seamless management and control of the construction process, facility management, environment monitoring, and other life cycle processes in the built environment. In recent years, the ability of real-time connectivity to online sensors deployed in an environment has led to the emergence of the concept of the Digital Twin of the built environment. The widespread adoption of Building Information Modeling (BIM) and the recent emergence of Internet of Things (IoT) applications offer several new insights and decision-making capabilities throughout the life cycle of the built environment. Finally, Extensive experiments are performed on the KITTI 3D object detection challenging benchmark to show the effectiveness of our fusion method and demonstrate that our deep fusion approach achieves state-of-the-art performance. In order to achieve this task, instead of densely combining the point-wise feature of the point cloud with the related pixel features, our fusion method novelly aggregates a small set of 3D Region of Interests (RoIs) in the point clouds with the corresponding 2D RoIs in the images, which are beneficial for reducing the computation cost and avoiding the viewpoint misalignment during the feature aggregation from different sensors. In this paper, a deep neural network architecture, named RoIFusion, is proposed to efficiently fuse the multi-modality features for 3D object detection by leveraging the advantages of LIDAR and camera sensors. When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensors (e.g., camera, LIDAR) is capable of mutually offering useful complementary information to enhance the robustness of 3D detectors. The proposed concept is currently being tested on a local test site to generate, update and adapt the Digital Twins as well as to incorporate additional semantic information about e.g. By combining available geodata with real-time sensor data from mobile construction machines, it is possible to create always-up-to-date Digital Twins of the relevant objects and processes in the field in order to facilitate supervision, additional planning steps, management, control and security activities. This paper presents a methodology for the development and application of Digital Twins representing and supporting the working environment of a construction site. However, the focus here lies mainly on the building itself and does not support the construction environment. One of the few recent successful examples is the introduction of the building information modeling (BIM) paradigm. P>The architecture, engineering and construction (AEC) industry appears hesitant to embrace new digital innovations.
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