From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings
Abstract
:Featured Application
Abstract
1. Introduction
2. Related Work
2.1. From Point Clouds to Building Information Model
2.2. Modelling of Building Geometry
2.2.1. Types of Models
2.2.2. Modelling of Indoor Environments
2.3. Object Recognition and Relationship Establishment
3. Full Description of the Approach
3.1. Project Framework
3.1.1. Buildings under Study
3.1.2. Input Data
- Scan stations:
- –
- optimize the placement of scan stations in order to acquire the whole environment without causing redundant data by multiplying scan stations
- –
- plan a consequent overlapping between point clouds in case of indirect georeferencing
- –
- if possible perform loops to improve the quality of the registration of point clouds (global adjustment) and to limit the risks of deviation
- Targets:
- –
- use at least 4 targets in common between two scans: 3 targets are sufficient to register point clouds issued from 2 successive scans but it is recommended to take more than 3 and to compensate the whole network of targets at once
- –
- prioritize targets placed in more than 2 scans. It should be ensured that targets are well-distributed in the space, that is to say placed at various ranges from the scanner, not in the same plane or along one line and locared at different heights
- –
- the use of natural targets such as edges, planes and cylinders can reduce and simplify data collection process; indeed, it can be difficult and time consuming to place targets throughout all the building
- Point spacing:
- –
- consider upstream the desired point density according to project specifications
- –
- adapt point spacing depending on space configuration and the positioning of targets in relation to the laser scanner
- Georeferencing after registration:
- –
- georeferencing can be either direct when positions and orientations of the successive scanner stations are known within a geodetic network, or indirect when the georeferencing is performed after the registration
- –
- in case of indirect georeferencing, the coordinates of targets or characteristic points well distributed in the scene are measured and are used to compute the transformation from the local system to the global system
- –
- georeferencing can improve the quality of the registration notably for a linear project
3.1.3. Overview of the Approach
3.2. Segmentation into Structural Elements
3.2.1. Segmentation into Sub-Spaces
3.2.2. Segmentations into Planes and Point Classification
3.3. Reconstruction of Structural Elements
3.3.1. Element Description into Obj Format
- Wall composed of one plane: It can be encountered when only one side of a wall is determined or for elements such as pillars. In this case a line is adjusted to the plane point cloud projected in an horizontal plane. The wall portion is described by two points in the X-Y plane.
- Wall composed of two planes: If the two planes do not describe the two sides of a wall, the method of the case 1 is used. Otherwise, two lines are adjusted to the two plane point clouds projected in the horizontal plane. The wall axis is determined by the average of the two lines and the half-thickness is calculated by the average of distances between the points of both planes and the axis of the wall. Based on the axis and the thickness of the wall, four points are constructed to describe the wall in the X-Y plane.
- Wall composed of more than two planes: In this case, planes located in each side of the wall are grouped. If only one side is identified, the method of case 1 is used. If two sides are identified, the method proposed in case 2 is considered.
3.3.2. File Generation into IFC Format
4. Assessment of the Developed Approach
4.1. Datasets and Thresholds Used for the Assessment
4.1.1. Datasets for the Approach Assessment
4.1.2. Review of Involved Thresholds
- Thresholds related to spatial resampling of point clouds
- Thresholds related to space dimensions
- Thresholds related to constraints and quality criteria
4.1.3. Processing of Datasets
4.2. Assessment of Segmentations and Point Classification
4.2.1. Segmentation into Sub-Spaces
4.2.2. Classification of Points into Several Categories
4.2.3. Segmentation into Planes
4.3. Assessment of Structural Element Reconstruction
4.3.1. Inspection of Reconstructed Walls
4.3.2. Attribution of Standard Deviations to Reconstructed Walls
4.3.3. Analysis of Points-to-Model Distances
5. Future Work
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
BIM | Building Information Modelling |
AEC | Architecture, Engineering and Construction |
TLS | Terrestrial Laser Scanning |
MEP | Mechanical, Electrical and Plumbing |
RANSAC | RANdom SAmple Consensus |
MW | Manhattan-World |
PCA | Principal Component Analysis |
MLESAC | Maximum Likelihood Estimation SAmple Consensus |
RMSE | Root Mean Square Error |
IFC | Industry Foundation Classes |
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Process | Threshold value |
---|---|
Spatial resampling and/or space dimensions | |
3D region growing in point clouds | voxel size = multiple of resampling |
Filter for 2D regions removal in binary image | 1 square metre (∼1100 pixels) |
Filter for 3D regions removal | 1.8 m (French “Carrez” law) |
1.8 m3 (92,000 points for a spatial resampling of 1 cm) | |
Minimal number of points for a plane | 1800 for a spatial resampling of 1 cm (10 cm × 1.80 m) |
Conditions to perform an additional plane segmentation (while loop) | number of remaining points: >5% () of initial point cloud OR > 400 () for a spatial resampling of 1 cm (20 cm × 20 cm) |
the last segmentation must have extracted at least one plane of 1800 points minimum (≥1) | |
Constraints and quality criteria | |
Maximum distance between inliers and plane | 2 cm for grounds and ceilings; 5 cm for walls |
RMSE of planes | 2 cm |
Horizontal and vertical constraints | maximal angular value of 1 degree ( et ) |
Criteria for the identification and the reconstruction of walls | maximal distance between planes between 5 cm () and 50 cm () |
parallelism: maximal angular value of 5 degrees |
Data | Individual House | Office Building |
---|---|---|
Results of floor segmentations | | |
Results of room segmentations (top view) | Garden level | 2nd floor |
Ceilings and Grounds | Walls | Objects | ||
---|---|---|---|---|
Individual house | TP | 99% | 89% | 94% |
FP | 1% | 11% | 6% | |
FN | 3% | 1% | 21% | |
Precision | 99% | 89% | 94% | |
Recall | 97% | 99% | 82% | |
Office building | TP | 99.5% | 93% | 81% |
FP | 0.5% | 7% | 19% | |
FN | 4.5% | 9% | 8% | |
Precision | 99.5% | 93% | 81% | |
Recall | 96% | 91% | 91% |
Number of Points | Mean of Deviations (cm) | Minimum Deviation (cm) | Maximum Deviation (cm) | ||
---|---|---|---|---|---|
Individual house | Garden level | 34 | 1.1 ± 0.8 | 0.2 | 4.4 |
1st floor | 23 | 0.9 ± 0.6 | 0.2 | 2.3 | |
Office building | Ground floor | 20 | 1.1 ± 0.6 | 0.4 | 2.7 |
1st floor | 24 | 1.2 ± 0.9 | 0.1 | 3.7 | |
2nd floor | 32 | 1.7 ± 1 | 0.6 | 4.1 | |
3rd floor | 10 | 1 ± 0.6 | 0.1 | 1.8 |
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Macher, H.; Landes, T.; Grussenmeyer, P. From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings. Appl. Sci. 2017, 7, 1030. https://doi.org/10.3390/app7101030
Macher H, Landes T, Grussenmeyer P. From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings. Applied Sciences. 2017; 7(10):1030. https://doi.org/10.3390/app7101030
Chicago/Turabian StyleMacher, Hélène, Tania Landes, and Pierre Grussenmeyer. 2017. "From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings" Applied Sciences 7, no. 10: 1030. https://doi.org/10.3390/app7101030
APA StyleMacher, H., Landes, T., & Grussenmeyer, P. (2017). From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings. Applied Sciences, 7(10), 1030. https://doi.org/10.3390/app7101030