Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds
Abstract
:1. Introduction
- (1)
- The precise and complete fitting of the wall surface remains problematic [5,25]. Because of the limited viewpoints and occlusion, the wall segments are often incomplete, especially in dense built-up areas. Additionally, due to the existence of windows and other building attachments, as well as the interruptions of shadow and vegetation, the wall points by stereo matching may not be strict vertical planes, leading to spurious or incomplete results.
- (2)
- Error-proneness of wall segment topology [26]. The intersection of adjacent walls and the searching of closed wall polygons require a clear description of wall–wall topology. Imperfect wall extraction results will result in incorrect connections, which require further interaction for industrial solutions. Furthermore, the regularization of the boundaries also requires a global description of wall topology.
2. Methods
2.1. Detection of Wall Segments
2.1.1. Wall Points Clustering
2.1.2. Non-Maximum Suppression
- (1)
- Rasterization
- (2)
- Non-Maximum Suppression
2.1.3. Multiple Cue Weighted RANSAC
2.1.4. Post-Processing
2.2. Graph-Based Wall Topology Analysis
2.2.1. Line Segment Topology Graph
2.2.2. Graph-Based Wall Connecting and Correction
- (a)
- Spurious edge, where the average point density is below the pre-given threshold . We simply delete the corresponding graph edge.
- (b)
- L type edge, where if two line segments are vertical and both need to be extended to the corner point or the length of exceeding part does not exceed a . The two line segments are directly intersected at the corner.
- (c)
- T type edge, where two line segments are vertical but do not form an L type edge. The bottom edge is extended to the intersected corner, while the top one is decomposed at the corner point and similar operations are adopted on the WLTG.
- (d)
- Z type edge, where two line segments are parallel. We add the missed short vertical edges, and, meanwhile, added one graph vertex and two graph edges to the WLTG.
2.2.3. Graph-Based Closed-Loop Analysis
3. Experiments and Evaluation
3.1. Dataset, Parameters and Metrics
3.2. Overall Results
3.3. Compare and Local Details
3.4. Limitation and Discussion
- (1)
- The non-closed walls caused by shared or occluded walls. As shown in location 2 of Figure 8, since our method detects the vector boundaries mainly based on the wall points and building roof from UAV-based point clouds, it will fail when buildings are adjacent or even connected, where no wall points are available. In such situations, the boundaries will be merged, which may require further manual additions in real applications to separate them.
- (2)
- For the gates and ancillary structures of the compound wall, i.e., in location 3 of Figure 8, since only one wall plane passes through the boundaries and polygons are detracted, our methods simply produce linear detection results, and the small polygons and the wall width are not considered. Further operations are still needed to meet the industry requirements of the cadastral survey.
- (3)
- Due to the influence of noise, the occlusion of vegetation, and various other factors, spurious edges may still appear and require further checking. In location 1 of Figure 8, it can be seen that a large amount of debris is usually piled up along the yard walls of rural houses; this may cause occlusion or spurious edges and require further checks in the process of actual production application.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
UAV | Unmanned Aerial Vehicle |
RANSAC | RANdom SAmple Consensus |
RTG | Roof Topology Graph |
WLTG | wall line segment topology Graph |
DBSCAN | Density-Based Spatial Clustering |
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Location | Point Density | Area Size | Ground Truth | ||
---|---|---|---|---|---|
Corners | Edges | Polygons | |||
Xi’An | 400 pts/m | 17,500 m | 371 | 350 | 63 |
Grid Size | Density | RANSAC | Roof Extraction | ||||
---|---|---|---|---|---|---|---|
DEM Hei | Planarity | Perc | |||||
Para | 0.1 m | 50 | 0.2 m | 10 | 2 m | 0.9 | 80% |
Area | Corners | Edges | Polygons | |||||||
---|---|---|---|---|---|---|---|---|---|---|
RMSE | Coor | Comp | Qua | Coor | Comp | Qua | Coor | Comp | Qua | |
a | 0.066 | 100.0 | 90.0 | 90.0 | 89.5 | 81.0 | 74.0 | 100 | 100 | 100 |
b | 0.065 | 90.0 | 85.7 | 78.2 | 90.0 | 85.7 | 78.2 | 100 | 100 | 100 |
c | 0.085 | 95.0 | 82.6 | 79.2 | 94.7 | 81.8 | 78.2 | 100 | 100 | 100 |
d | 0.069 | 93.9 | 89.6 | 84.7 | 93.2 | 87.2 | 82.0 | 100 | 88.9 | 88.9 |
e | 0.093 | 91.7 | 84.0 | 78.1 | 90.4 | 84.4 | 77.5 | 90.0 | 75.0 | 69.2 |
f | 0.086 | 94.1 | 83.5 | 79.3 | 93.5 | 86.3 | 81.4 | 88.9 | 80.0 | 72.7 |
average | 0.084 | 93.2 | 84.9 | 79.9 | 92.0 | 85.1 | 79.2 | 92.9 | 82.5 | 77.6 |
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Xu, B.; Han, Z.; Chen, M. Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds. Appl. Sci. 2023, 13, 10197. https://doi.org/10.3390/app131810197
Xu B, Han Z, Chen M. Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds. Applied Sciences. 2023; 13(18):10197. https://doi.org/10.3390/app131810197
Chicago/Turabian StyleXu, Bo, Zhaochen Han, and Min Chen. 2023. "Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds" Applied Sciences 13, no. 18: 10197. https://doi.org/10.3390/app131810197
APA StyleXu, B., Han, Z., & Chen, M. (2023). Precise Cadastral Survey of Rural Buildings Based on Wall Segment Topology Analysis from Dense Point Clouds. Applied Sciences, 13(18), 10197. https://doi.org/10.3390/app131810197