Next Article in Journal
Quality Assessment of Acquired GEDI Waveforms: Case Study over France, Tunisia and French Guiana
Next Article in Special Issue
Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels
Previous Article in Journal
Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing
Article

Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor

1
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2
School of Geosciences and Info Physics, Central South University, Changsha 410083, China
3
Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS B3P 2M6, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Belen Riveiro
Remote Sens. 2021, 13(16), 3146; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163146
Received: 23 June 2021 / Revised: 27 July 2021 / Accepted: 3 August 2021 / Published: 9 August 2021
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) scans and photogrammetric point clouds. To this end, we calculate the confidence property at multiple scales for an individual point cloud to measure the point cloud’s quality. The confidence property is utilized in the definition of the gradient for each point. We encode the individual point gradient structure tensor, whose eigenvalues reflect the gradient variations in the local neighborhood areas. The critical point clouds representing the building façade and rooftop (if, of course, such rooftops exist) contours are then extracted by jointly analyzing dual-thresholds of the gradient and gradient structure tensor. Based on the requirements of compact representation, the initial obtained critical points are finally downsampled, thereby achieving a tradeoff between the accurate geometry and abstract representation at a reasonable level. Various experiments using representative buildings in Semantic3D benchmark and other ubiquitous point clouds from ALS DublinCity and Dutch AHN3 datasets, MLS TerraMobilita/iQmulus 3D urban analysis benchmark, UAV-based photogrammetric dataset, and GeoSLAM ZEB-HORIZON scans have shown that the proposed method generates building contours that are accurate, lightweight, and robust to ubiquitous point clouds. Two comparison experiments also prove the superiority of the proposed method in terms of topological correctness, geometric accuracy, and representation compactness. View Full-Text
Keywords: critical points; gradient; gradient structure tensor; simplification; building façade; Semantic3D; DublinCity; Dutch AHN3; potogrammetric point clouds; GeoSLAM critical points; gradient; gradient structure tensor; simplification; building façade; Semantic3D; DublinCity; Dutch AHN3; potogrammetric point clouds; GeoSLAM
Show Figures

Graphical abstract

MDPI and ACS Style

Chen, D.; Li, J.; Di, S.; Peethambaran, J.; Xiang, G.; Wan, L.; Li, X. Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor. Remote Sens. 2021, 13, 3146. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163146

AMA Style

Chen D, Li J, Di S, Peethambaran J, Xiang G, Wan L, Li X. Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor. Remote Sensing. 2021; 13(16):3146. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163146

Chicago/Turabian Style

Chen, Dong, Jing Li, Shaoning Di, Jiju Peethambaran, Guiqiu Xiang, Lincheng Wan, and Xianghong Li. 2021. "Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor" Remote Sensing 13, no. 16: 3146. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163146

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop