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Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models
Computer Vision and Pattern Recognition · Submitted 23 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This work offers a practical solution for rapid and accurate urban GeoBIM construction.
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The abstract, sentence by sentence
Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s1 | Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. | background 1.00 | 0.06 | 0.02 | asserted 0.60 |
| s2 | However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. | background 0.66 | 0.07 | 0.02 | asserted 0.99 |
| s3 | BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. | background 0.52 | 0.07 | 0.02 | asserted 0.98 |
| s4 | In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. | contribution 0.99 | 0.69 | 0.10 | asserted 0.98 |
| s5 | A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. | method 0.98 | 0.78 | 0.03 | asserted 0.97 |
| s6 | A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. | method 0.99 | 0.92 | 0.04 | asserted 0.99 |
| s7 | Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. | result 1.00 | 0.96 | 0.42 | asserted 1.00 |
| s8 | This work offers a practical solution for rapid and accurate urban GeoBIM construction. | interpretation 0.93 | 0.39 | 0.73 | asserted 0.98 |
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The paper
Original abstract
Developments in three-dimensional real worlds promote the integration of geoinformation and building information models (BIM) known as GeoBIM in urban construction. Light detection and ranging (LiDAR) integrated with global navigation satellite systems can provide geo-referenced spatial information. However, constructing detailed urban GeoBIM poses challenges in terms of LiDAR data quality. BIM models designed from software are rich in geometrical information but often lack accurate geo-referenced locations. In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes. A state-of-the-art deep learning framework and graph theory are first combined for LiDAR point cloud segmentation. A coarse-to-fine matching program is then developed to integrate object point clouds with corresponding BIM models. Results show the overall segmentation accuracy of LiDAR datasets reaches up to 90%, and average positioning accuracies of BIM models are 0.023 m for pole-like objects and 0.156 m for buildings, demonstrating the effectiveness of the method in segmentation and matching processes. This work offers a practical solution for rapid and accurate urban GeoBIM construction.
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