-Generation and quality of as built models influence the subsequent applications such as progress monitoring, quality control, and deviation detection. The quality of any 3D reconstructed model heavily depends on the raw inputs and the post processing involved. While laser and LiDAR-based scanning are widely prevalent, lower cost equipment and sensors are increasingly becoming adaptable for 3D reconstruction. This study tests the feasibility of using IR-based scanning tablets and passive stereo vision cameras to acquire data from a construction environment based on the type of applications (such as progress monitoring, deviation detection) on a construction site. Two different off the shelf technologies: Tango tablet and ZED camera are tested during this research for developing as-built models using 3D reconstruction. The devices are compared on the basis of metrics such as preparation time for each scan, calibration of the scanner and total scanning time for determining the ease of scanning process, accuracy of generated point clouds. Also, the influence of external factors such as scanning parameters, ambient lighting, and characteristics of the object being scanned, and angle/orientation of scanner with respect to the object are studied.
Information retrieval and automated progress estimation of ongoing construction projects have been an area of interest for researchers in the field of civil engineering. It is done using 3D point cloud asbuilt and as-planned model. Advancements in the field of photogrammetry and computer vision have made 3D reconstruction of buildings easy and affordable. But the high variability of construction sites, in terms of lighting conditions, material appearance, etc. and error-prone data collection techniques tend to make the reconstructed 3D model erroneous and incorrect representation of the actual site. This eventually affects the result of progress estimation step. To overcome these limitations, this paper presents a novel approach for improving the results of 3D reconstruction of a construction site by employing two-step process for the reconstruction as compared to the traditional approach. In the proposed method, the first step is to obtain an as-built 3D model of the construction site using 3D scanning techniques or photogrammetry in the form of point cloud data. In the second step, the model is passed through pre-trained machine learning binary classification model for identifying and removing erroneous data points in the captured point cloud. Erroneous points are removed by identifying the correct building points. This processed as-built model is compared with an as-planned model for progress estimation. Based on the proposed method, experiments are carried out using commercially available stereo vision camera for 3D reconstruction.
A major bottleneck activity in the process of restoration of Heritage Structures is the reassembly of its fragments. Computer-aided reassembly could assist in finding the relation between them thereby reducing time, manpower and potential degradation to fragile fragments. Using geometric compatibility between the adjacent fragments as the central idea, a reassembly framework for a three-dimensional shell is proposed as a logical extension of the twodimensional framework. Edges are extracted as polygons and relevant features are computed at each of its vertices. Sequences of the match for two fragments in the feature space are found using a modified version of Smith-Waterman Algorithm. Each match is assessed using a connectivity score. The final choice of best match is left to the user by displaying the resultant assembled fragments of prospective candidates along with the score. After pairwise matching, the global reassembly is done through a clustering-based method. This framework can handle fragments even with curved edges which can be reasonably approximated by a set of edges. We verify the methodology using a simulated dataset for both 2D pieces and a shattered 3D surface object.
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