Abstract
To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and the extraction of the relationship among different elements due to the complicated indoor environment. This is even more difficult for the low-quality point cloud generated by low-cost scanning equipment. This paper proposes an automatic as-built BIMs generation framework that transforms the noisy 3D point cloud produced by a low-cost RGB-D sensor (about 708 USD for data collection equipment, 379 USD for the Structure sensor and 329 USD for iPad) to the as-built BIMs, without any manual intervention. The experiment results show that the proposed method has competitive robustness and accuracy, compared to the high-quality Terrestrial Lidar System (TLS), with the element extraction accuracy of 100%, mean dimension reconstruction accuracy of 98.6% and mean area reconstruction accuracy of 93.6%. Also, the proposed framework makes the BIM generation workflows more efficient in both data collection and data processing. In the experiments, the time consumption of data collection for a typical room, with an area of 45–67 , is reduced to 4–6 min with an RGB-D sensor from 50–60 min with TLS. The processing time to generate BIM models is about half minutes automatically, from around 10 min with a conventional semi-manual method.
Highlights
Building information models (BIMs), including as-designed BIMs (AD BIMs) and as-built BIMs (ABBIMs), are the digital representations for the whole life cycle management from design, construction to demolition [1], which are widely used in the areas of construction management [2], computer games [3], indoor navigation [4], and emergency response [5]
Recent development makes the generation of indoor as-built building information models (AB BIMs) more efficient and convenient, there are still many problems need to be overcome: (1) current approaches are designed for high-quality point cloud and are impotent for the low-quality dataset from low-cost sensors, i.e., RGB-D
We proposed an automatic and efficient indoor AB BIMs generation framework by using low-cost RGB-D sensors
Summary
Building information models (BIMs), including as-designed BIMs (AD BIMs) and as-built BIMs Recent development makes the generation of indoor AB BIMs more efficient and convenient, there are still many problems need to be overcome: (1) current approaches are designed for high-quality point cloud and are impotent for the low-quality dataset from low-cost sensors, i.e., RGB-D sensors; (2) traditional method is inefficient and restricted because additional manual intervention is required in semantization and digitization; (3) the extraction of digital spatial and relationship information still not been addressed, especially for the low-quality point cloud dataset. Inspired by the success of deep-learning technology in the semantic segmentation areas, we modify the fully convolutional network (FCN) to extract the attribute information of the elements (e.g., wall, ceiling, floor, windows, and door) from low-quality RGB-D data (color and depth images) [27]. 2020, 20, a neural network is established for the 2D image semantic segmentation in the training procedure. of 21
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