Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Model-based object recognition in dense-range images—a review
ACM Computing Surveys (CSUR)
Recognition of object classes from range data
Artificial Intelligence - Special volume on computer vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A coarse to fine 3D registration method based on robust fuzzy clustering
Computer Vision and Image Understanding
Registering Multiview Range Data to Create 3D Computer Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Engineering Informatics
Preface: Special issue on construction informatics
Advanced Engineering Informatics
Toward automated generation of parametric BIMs based on hybrid video and laser scanning data
Advanced Engineering Informatics
Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features
Advanced Engineering Informatics
Plane-based registration of construction laser scans with 3D/4D building models
Advanced Engineering Informatics
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Advanced Engineering Informatics
An automated stabilisation method for spatial to structural design transformations
Advanced Engineering Informatics
Towards precise real-time 3D difference detection for industrial applications
Computers in Industry
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The construction industry lacks solutions for accurately, comprehensively and efficiently tracking the three-dimensional (3D) status of buildings under construction. Such information is however critical to the successful management of construction projects: It supports fundamental activities such as progress tracking and construction dimensional quality control. In this paper, a new approach for automated recognition of project 3D Computer-Aided Design (CAD) model objects in large laser scans is presented, with significant improvements compared to the one originally proposed in Bosche et al. (in press) [11]. A more robust point matching method is used and registration quality is improved with the implementation of an Iterative Closest Point (ICP)-based fine registration step. Once the optimal registration of the project's CAD model with a site scan is obtained, a similar ICP-based registration algorithm is proposed to calculate the as-built poses of the CAD model objects. These as-built poses are then used for automatically controlling the compliance of the project with respect to corresponding dimensional tolerances. Experimental results are presented with data obtained from the erection of an industrial building's steel structure. They demonstrate the performance in real field conditions of the model registration and object recognition algorithms, and show the potential of the proposed approach for as-built dimension calculation and control.