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

Benedikt Schwab; Thomas H. Kolbe

cs.CVMar 11, 2026

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

Authors: Benedikt Schwab, Thomas H. Kolbe

Organizations: Chair of Geoinformatics, TUM School of Engineering and Design, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany

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

Figures & tables

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. 0 0 0 Delorean by Luis Adrian licensed under CC-BY-4.0 .

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 $20$ , $90$ , $4.3$ , and $53\text{\,}\mathrm{\%}$ 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 Audi Autonomous Driving Dataset (A2D2) equipped with 6 cameras and 5 LiDAR sensors ( Geyer et al., 2020 ) .

	Campaign 1	Campaigns 2–4
		C2	C3	C4
Start time	13:06:26	12:16:47	12:27:22	12:34:11
Duration	$90\text{\,}\mathrm{s}$	$98\text{\,}\mathrm{s}$	$107\text{\,}\mathrm{s}$	$118\text{\,}\mathrm{s}$
Date	14 January 2020	18 November 2020
Astronomical season	early winter	late autumn
Weather	sunny, few clouds	sunny, few clouds

Table 1: Environmental conditions during the four sensor data acquisition campaigns. Parameter values marked with

\dagger

are derived from historical meteorological records ( Time and Date AS, 2025 ) .

Figure 6: UML diagram of the 3DSensorDB database schema in green alongside a selected set of CityGML classes depicting the association between individual LiDAR beams and their thematic surface from which they were reflected. Due to the mapping to database tables, the 3DSensorDB classes use PostgreSQL data types. Classes of the CityGML 3.0 space concept are colored blue and two classes of thematic modules are shown in yellow as examples.

Figure 7: Raycasting-based association approach between LiDAR beam and object surface that is implemented on the 3DSensorDB leveraging the 3DCityDB.

Figure 8: Intensity values measured on the four Spectralon strips as a function of range

R

with the zenith angle

\theta<3^{\circ}

, where a point denotes the mean intensity and the shaded region indicates the standard deviation.

Figure 9: Individual intensity values measured as a function of the zenith angle $\theta$ at a 2-meter distance from the Spectralon rotation center with approximated cosine functions.

Figure 10: Bird’s-eye view of the reference point clouds and the point clouds of each SLAM submap from the acquisition campaigns after coregistration. Variations in submap coverage across campaigns necessitate combining the sensor data using each platform’s georeferenced pose trajectory, which is also required for associating the sensor data with the semantic road space model.

	Campaign 1			Campaign 2			Campaign 3			Campaign 4
ET	1 year 4 months			1 year 9 months			1 year 9 months			1 year 9 months
$\sum$	91.1M points			109.6M points			104.5M points			113.2M points
SM	#	$f$	RMSE	#	$f$	RMSE	#	$f$	RMSE	#	$f$	RMSE
1	7.1M	96.4%	0.18m	7.3M	96.2%	0.21m	6.3M	99.3%	0.16m	6.1M	95.9%	0.22m
2	8.2M	99.3%	0.11m	9.5M	98.9%	0.14m	6.8M	95.9%	0.21m	7.4M	98.3%	0.13m
3	7.8M	99.1%	0.15m	9.5M	99.0%	0.15m	8.0M	99.2%	0.12m	10.8M	94.7%	0.19m

Table 2: Fitness and RMSE scores of the point cloud submaps of each campaign after coregistration with the reference point cloud. ET = Elapsed time since reference surveying campaign, SM = submap of graph-based SLAM.

Figure 11: Distribution of the

15\,816

thematic objects in the LOD 3 road space model broken down by CityGML 3.0 classes (top diagram). The model’s rich semantic information supports fine subdifferentiations (bottom diagrams) and therefore enables flexible grouping of objects and their associated sensor data.

	$r\leq 15\,\text{meter}$			$r\leq 30\,\text{meter}$			all ranges
	associated		all	associated		all	associated		all
Camp. 1	79.6%	58.3M	73.2M	75.5%	68.8M	91.1M	74.6%	71.2M	95.5M
Camp. 2	75.6%	65.9M	87.2M	70.8%	77.6M	109.6M	69.8%	80.3M	115.0M
Camp. 3	76.9%	66.8M	86.8M	72.2%	75.5M	104.5M	71.3%	77.5M	108.8M
Camp. 4	75.1%	72.4M	96.4M	71.9%	81.4M	113.2M	70.9%	83.4M	117.5M
$\Sigma/\mu$	76.8%	263.5M	343.6M	72.6%	303.2M	418.4M	71.7%	312.4M	436.8M

Table 3: Distribution of associated points broken down by campaign and range threshold. Points within a 15-meter range are associated more frequently than those beyond this threshold.

Figure 12: Association distribution of LiDAR points according to the CityGML 3.0 classes (top) and distribution of total object observations (bottom). Our method automatically associated 312.4 million points and identified

87\,345

object observations, each counted individually per sensor and campaign.

Figure 14: Selection of walls A and B for point-level analyses, as they were comprehensively captured by the sensors at varying ranges and angles. Additional cropping ensures that only points reflected from the same material are included.

Figure 15: t-SNE analysis at a perplexity of $200$ for $10\,000$ points reflected by the rear-left sensor on wall A. While the zenith angle strongly influences the measured intensity, no such effect is observed for the azimuth angle.

Figure 16: t-SNE analysis for the wall paint (left) and wall base plinth (right) of wall B at perplexity 100 revealing that the measured intensities are influenced by the sensor, range, and campaign.


1. SolitaryVegetationObject	0.0	3.8	13.2	9.2	10.8	114.9	124.3
2. SolitaryVegetationObject		0.0	9.7	12.7	14.1	118.3	127.7
3. DoorSurface			0.0	22.2	23.8	127.8	137.3
4. WallSurface				0.0	4.6	106.5	115.7
5. WallSurface					0.0	104.2	113.6
6. CityFurniture						0.0	13.9

Table 4: RMSE-based distance

\text{dist}_{\text{Q3}}

quantifying differences between the radiometric fingerprints of only those objects in Figure 17 that were fully covered by the sensor throughout all zenith-angle intervals

[\theta_{j},\theta_{j+1})

CityGML class	count
AuxiliaryTrafficArea	6	7.5	33.0	91.9	33.3	34.1	22.4	14.7	40.0	31.2
BuildingInstallation	84		31.4	77.7	32.5	25.7	25.7	35.2	34.4	31.1
CityFurniture	193			78.5	77.4	73.9	78.8	93.5	75.5	79.1
DoorSurface	37				33.3	27.4	27.0	36.8	34.3	32.3
OuterFloorSurface	1						16.5	33.1	26.2	25.0
SolitaryVegetationObject	39						15.6	25.3	29.4	25.0

Table 5: Mean distance

\overline{d}_{\text{Q3}}

between the radiometric fingerprints of objects grouped by CityGML class indicating class-specific material composition patterns.

function of object	count
bench	5	25	28	28	40	53	20	70	49	50	96	123	119	70	22
controller box	4		27	37	35	60	31	58	50	35	83	106	102	62	22
fence	3			34	47	51	24	80	51	58	105	132	129	76	31
other	11				47	66	42	62	58	41	82	102	99	68	34
pole	4					79	47	89	65	75	103	122	120	82	54
railing	13						12	78	48	57	103	131	127	73	22

Table 6: Mean distance

\overline{d}_{\text{Q3}}

between radiometric fingerprints of CityFurniture objects grouped by function demonstrating function-specific material patterns.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Parameter	Specification
Channels	$16$
Field of View: Vertical	$30\text{\,}\mathrm{\SIUnitSymbolDegree}$ ( $-15\text{\,}\mathrm{\SIUnitSymbolDegree}$ to $15\text{\,}\mathrm{\SIUnitSymbolDegree}$ )
Measurement range	$100\text{\,}\mathrm{m}$
Field of View: Horizontal	$360\text{\,}\mathrm{\SIUnitSymbolDegree}$
Rotation Rate	$520\text{\,}\mathrm{Hz}$ ( $10\text{\,}\mathrm{Hz}$ used for experiments)
Range accuracy	up to ± $3\text{\,}\mathrm{cm}$ (typical)

Appendix

Table 7: Specifications of the VLP-16 LiDAR sensor by Velodyne Lidar, Inc. (2022) .

Figure 25

Figure 26

Parameter	Value
Line segment length $\ell$	$1\text{\,}\mathrm{m}$
Spherocylinder radius $\rho$	$5\text{\,}\mathrm{cm}$
Max. object surface associations per point	1

Appendix

Table 8: Parameter values utilized for the beam-surface association process.

Parameter	Value
Range $r_{k}$	$[0\text{m},15\text{m})$
Zenith angle $\theta_{k}$	$\left\{[0^{\circ},20^{\circ}),\ [20^{\circ},40^{\circ}),\ [40^{\circ},60^{\circ}),\ [60^{\circ},90^{\circ})\right\}$
Azimuth angle $\phi_{k}$	$[0,2\pi)$
$\text{Campaign}_{k}$	campaign 1
$\text{Sensor}_{k}$	rear-left sensor
$\text{Object}_{k}$	selected objects highlighted in Figure 17

Appendix

Table 9: Filters applied according to subsection 4.6 for extracting radiometric fingerprints, as discussed in Section 4.6 and illustrated in Figure 17 and Table 4 .

Parameter	Value
Range $r_{k}$	$[0\text{m},15\text{m})$
Zenith angle $\theta_{k}$	$\left\{[0^{\circ},20^{\circ}),\ [20^{\circ},40^{\circ}),\ [40^{\circ},60^{\circ}),\ [60^{\circ},90^{\circ})\right\}$
Azimuth angle $\phi_{k}$	$[0,2\pi)$
$\text{Campaign}_{k}$	campaign 1
$\text{Sensor}_{k}$	front-center sensor
$\text{Object}_{k}$	–

Appendix

Table 10: Filters applied according to subsection 4.6 for extracting radiometric fingerprints, as discussed in Section 4.7 and listed in Table 5 and 6 .

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