Presentation for the 2022 EuroCarto contribution on "Non-Photorealistic Rendering of 3D Point Clouds for Cartographic Visualization".

1. Non-Photorealistic Rendering of 3D Point
Clouds for Cartographic Visualization
Ole Wegen, Jürgen Döllner, Ronja Wagner, Daniel Limberger, Rico Richter, Matthias Trapp
Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany
Raw Point Cloud Tree Instance Segmentation Non-Photorealistic Point Cloud Rendering

2. Introduction – Point Clouds
• Point clouds are a common representation
of 3D real-world data
• Acquired using specific laser scanning hardware
or photogrammetry approaches
• Observation: increasing acquisition efficiency,
detail, and analysis efficiency
• Application domains: city planning,
infrastructure/environmental monitoring, facility
management
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Point cloud from aerial LiDAR, provided by “Landesvermessung und Geobasisinformation
Brandenburg”, approx. 10-20 points per m2.

3. Introduction – Point Cloud Rendering
• Point clouds as data source for applications that
uses real-world geometric and geographic data
• Direct, interactive visualization/exploration of
raw point cloud data possible but of limited
usefulness for cartography-related tasks
• What techniques can we employ to overcome the
challenges of point cloud data for cartography-related
tasks?
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Plan of Paris with numerous vignettes depicting significant buildings and Metro lines.
Published by F. Dutal, 1920[1].

4. Challenges Regarding Raw Point Clouds
1. Acquisition-related problems:
point sparsity, dull colors, noise
2. Missing depth cues complicate the
distinction between different objects
3. Increased cognitive/perceptual effort for
information retrieval/scene understanding
due to missing abstraction leads to
4. Missing semantic information complicates
detail+overview/focus+context approaches
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Point cloud from aerial LiDAR, obtained from OpenGeodata.NRW[2], approx. 75 points per m2.

5. Contributions
Objective: Motivate and highlight the direct use of 3D point clouds for cartographic purposes
Contributions:
1. Demonstrate application of point clouds for cartography, without computation of intermediate
representations
2. General approach for point cloud visualization in the cartographic context, combining
• Analysis techniques for massive point clouds
• Various non-photorealistic rendering technique for stylized depiction of point clouds
• Interactive parameter control of exploration and rendering techniques
3. Applications:
• Interactive exploration of real-world scenes,
• Facilitate perception/recognition of specific semantic classes
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6. Overview Visualization Pipeline
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Interactive, user-driven visualization of point clouds for cartographic purposes

7. Example Pipeline – Results
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Original Point Cloud Final Abstraction Result

8. Overview Visualization Pipeline
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Interactive, user-driven visualization of point clouds for cartographic purposes
Preprocessing

9. Point Cloud Analysis & Preprocessing
Prepare point cloud in an initial (potentially time consuming) step:
1. Enrich point cloud with shading and segment information
2. Split point cloud into multiple point clouds for separate parameterization
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10. Example Pipeline – Analysis & Preprocessing
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11. Analysis & Preprocessing – Segmentation (I)
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Results of semantic segmentation using an algorithmic approach.
Albedo
Data
Infrared
Data
Surface
Orientation
Data

12. Analysis & Preprocessing – Segmentation (II)
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Semantic segmentation of mobile mapping data using machine learning (PointNet++[3] and EdgeConv[4])

13. Analysis & Preprocessing – Ambient Occlusion
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Input point cloud with colors obtained by aerial images. Depiction of ambient occlusion term only.

14. Analysis & Preprocessing – Ambient Occlusion
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15. Overview Visualization Pipeline
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Interactive, user-driven visualization of point clouds for cartographic purposes
Rendering

16. Interactive Point Cloud Rendering
• Rendering result of each point cloud is controlled by a set of rendering parameters
• A configuration of parameter values is stored in the form of a stylization descriptor
• High-level control: user selects and assigns stylization descriptors
• Fine-grained control: user manipulates individual parameters
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17. Example – Rendering
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18. Overview Visualization Pipeline
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Interactive, user-driven visualization of point clouds for cartographic purposes
Postprocessing

19. Postprocessing
• Combine different rendering results
• Visual enhancement using image processing
• Outlining for improved object perception and as depth cues
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20. Example – Postprocessing
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21. Visualization Pipeline
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22. Application Examples (I)
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23. Application Examples (II)
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Change visualization
in an urban area

24. Application Examples (III)
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Focus+Context:
Highlight instances of
specific semantic class
(e.g., pedestrians)

25. Conclusions
• Point clouds have the potential to serve as basis for generation of cartographic visualizations
• Segmentation is an important preprocessing step for the application of rendering techniques
• NPR can be used to provide abstraction, enhance perception, and reduce the cognitive effort
• Low and high-level stylization parameters enable fast configuration of NPR techniques
• 3D visualization is not always the best choice for cartographic applications, but where it is, point clouds
are a useful geometry representation when combined with analysis and NPR techniques
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26. References
• [1] Paris Monumental et Metropolitain,
https://commons.wikimedia.org/wiki/File:1920_Art_Nouveau_Monument_Map_of_Paris,_France_-
_Geographicus_-_ParisMonumental-dutal-1920.jpg
• [2] OpenGeoData.NRW, www.opengeodata.nrw.de
• [3] Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet++: Deep Hierarchical Feature
Learning on Point Sets in a Metric Space. NIPS 2017.
• [4] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, Justin M. Solomon. Dynamic
Graph CNN for Learning on Point Clouds. 2019. ACM Trans. Graph. 38(5).
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