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Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models

Benedikt Schwab, Thomas H. Kolbe

Abstract

Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb

Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models

Abstract

Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb
Paper Structure (33 sections, 22 equations, 15 figures, 10 tables)

This paper contains 33 sections, 22 equations, 15 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Background and related work
  3. Interoperable and semantic modeling of road space
  4. LiDAR datasets
  5. Physical principles of laser scanning intensity
  6. Bidirectional reflectance distribution functions
  7. Material property databases
  8. LiDAR simulators
  9. Radiometric correction and calibration
  10. Research gap and questions
  11. Methodology
  12. Radiometric accuracy analysis in the laboratory
  13. Semantic road space model in LOD3
  14. Road network and roadside objects
  15. Building models
  16. ...and 18 more sections

Figures (15)

  • Figure 1: Deriving radiometric fingerprints by grouping electromagnetic radiation responses of object surfaces measured by multiple LiDAR sensors across varying distances, angles, and environmental conditions to infer surface-specific properties and material composition information.
  • Figure 2: The workflow of this study.
  • Figure 3: Controlled intensity measurements were conducted with the Velodyne VLP-16 LiDAR sensor and a Spectralon target featuring four calibrated diffuse reflectance strips of approximately 20.0, 90.0, 4.3, and 53% for varying distances and angles of incidence.
  • Figure 4: Semantic model of the road spaces in the city of Ingolstadt in Germany according to CityGML 3.0.
  • Figure 5: Vehicle of the A2D2 equipped with 6 cameras and 5 LiDAR sensors geyerA2D2AudiAutonomous2020.
  • ...and 10 more figures