New Point Clouds, Segments, Semantics and Automation · 2020. 6. 2. · • 2D floorplan generation...
Transcript of New Point Clouds, Segments, Semantics and Automation · 2020. 6. 2. · • 2D floorplan generation...
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Point Clouds, Segments, Semantics and Automation
Florent Poux
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Florent Poux - PC, Segments, Semantic and AutomationNCG Point Cloud 2020 2
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Florent Poux - PC, Segments, Semantic and Automation
DigitizationReal
world
Transformation (additional info from domain or
app.
Low-leveldigital model
UsageInterpretation
High-leveldigital model
Context
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3D Point Cloud Specificities
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Representation & Structuration
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Automation
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Video Source : EmescentNCG Point Cloud 2020 Florent Poux - PC, Segments, Semantic and Automation 10
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Image source: LGENCG Point Cloud 2020 Florent Poux - PC, Segments, Semantic and Automation 11
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Image source: ETH ZürichNCG Point Cloud 2020 Florent Poux - PC, Segments, Semantic and Automation 12
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DOOR
TABLECHAIR
CLUTTER
WALL
BOOKCASE
BEAM
BLACKBOARD
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- 3D Point Cloud Specificities- Representation & Structuration- Automation
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DELIVERY PRODUCTION (Floor plan, Mesh, …),
SIMULATION, …
Semantics & Knowledge Integration
KNOWLEDGE
TODAY
WHAT WE WANT
INFORMATION EXTRACTION based on
ReasoningFlorent Poux - PC, Segments, Semantic and AutomationNCG Point Cloud 2020 18
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How to extract and integrateknowledge within 3D point clouds for autonomous decision-making
systems?
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Point Cloud Specificity
Unstructured and too sparsefor DBMS per-row insertion
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Point SemanticPatch
ConnectedElement
ClassInstance
AggregatedElement
Knowledge
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Gestalt’s theory
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Visual patterns on points
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Visual patterns on pointsDeep learning > feature-engineering
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Point Cloud Datasets
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Point Cloud DatasetsDeep learning < feature-engineering
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Overall
Precision
Ceiling Floor Wall Beam Door Table Chair Bookcase
0 1 2 3 6 7 8 10
Baseline (no
colour) [16]0.48 0.81 0.68 0.68 0.44 0.51 0.12 0.52
Baseline
(full) [16]0.72 0.89 0.73 0.67 0.54 0.46 0.16 0.55
Ours 0.94 0.96 0.79 0.53 0.19 0.88 0.72 0.2
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Overall
Precision
Ceiling Floor Wall Beam Door Table Chair Bookcase
0 1 2 3 6 7 8 10
Baseline (no
colour) [16]0.48 0.81 0.68 0.68 0.44 0.51 0.12 0.52
Baseline
(full) [16]0.72 0.89 0.73 0.67 0.54 0.46 0.16 0.55
Ours 0.94 0.96 0.79 0.53 0.19 0.88 0.72 0.2
10 million points / minute
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Isolation
Relation « host/guest »
Extract
Knowledge-driven
Context / Proximity
Relationships / Topology
GeometriesGeneralizations
Local Features
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A classified entity
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chair
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Connected Elements
▪ Aggregated-Element▪ Normal-Element▪ Sub-Element
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Chair = AE
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Part segmentation
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Characterization refinement
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Semantic Representation
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How to extract and integrateknowledge within 3D point clouds for autonomous decision-making
systems?
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1. Using a multi-level conceptual structure
2. Parsing PC at the lowest possible level
3. Plug a domain formalization through an
ontology of classification
4. Generate a modular semantic
representation
… Automatically …NCG Point Cloud 2020 Florent Poux - PC, Segments, Semantic and Automation 53
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V R A P P L I C AT I O N
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A R A P P L I C AT I O N
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The SPC in 5 points
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- Interoperable point cloud data structure…
- … leveraged for automated object detection…
- … providing a large domain connectivity…
- … unsupervised and robust to variability…
- … modular and efficient.
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- Define powerful SPC-based AI Agents
- Increase generalization / specialization
- Dynamic data and LoD management
- Enhance unsupervised segmentation
- Enhance classification
- Integrate natural processesNCG Point Cloud 2020 Florent Poux - PC, Segments, Semantic and Automation 62
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This Special Issue aims to submissions involves advancement of data mining
and the infatuation of reality capture will continue to push the research
communities forward. Particularly, ways to obtain high-quality application-
oriented labelled datasets will permit a wider dissemination of robust learning
approaches. Henceforth, we encourage authors to submit original research
articles, review papers and case studies from both theoretical and application-
oriented perspectives on this significant and exciting subject. In more details,
topics suitable for this Special Issue including but not limited to:
Special Issue
Automatic Feature Recognition from Point Clouds
Special Issue Editors:
Florent Poux : University of Liège, Belgium
Roland Billen: University of Liège, Belgium
https://www.mdpi.com/journal/ijgi/special_issues/GIS_point_clouds
IMPACT FACTOR
1.840
MDPI St. Alban-Anlage 66 CH-4052 Basel Switzerland Tel: +41 61 683 77 34 Fax: +41 61 302 89 18
Academic Open Access Publishing Since 1996
• Georeferenced point clouds from laser scanners (mobile, hand-held, backpack-mounted, terrestrial, aerial)
• Point clouds from panoramas, phone/cameras images, oblique and satellite imagery• Point Cloud segmentation, classification, semantic enrichment for application-driven scenario• Point Cloud structuration and knowledge integration• Point Cloud Knowledge extraction and high-performance feature extraction for large-scale datasets• 2D floorplan generation of indoor point clouds• Industrial applications with large-scale point clouds• Feature-based rendering and visualization of large-scale point clouds• Deep learning for point cloud processing
(Submission Deadline: Extended to 30 September 2020)
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Florent Poux - PC, Segments, Semantic and Automation
1. Poux, F. The Smart Point Cloud: Structuring 3D intelligent point data, Liège, 2019.
2. Poux, F.; Billen, R. Voxel-Based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep
Learning Methods. ISPRS Int. J. Geo-Information 2019, 8, 213.
3. Poux, F.; Neuville, R.; Van Wersch, L.; Nys, G.-A.; Billen, R. 3D Point Clouds in Archaeology: Advances in Acquisition, Processing and
Knowledge Integration Applied to Quasi-Planar Objects. Geosciences 2017, 7, 96.
4. Poux, F.; Billen, R. Smart point cloud: Toward an intelligent documentation of our world. In Proceedings of the PCON; Liège, 2015; p. 11.
5. Poux, F.; Neuville, R.; Hallot, P.; Billen, R. Point clouds as an efficient multiscale layered spatial representation. In Proceedings of the
Eurographics Workshop on Urban Data Modelling and Visualisation; Vincent, T., Biljecki, F., Eds.; The Eurographics Association: Liège,
Belgium, 2016.
6. Poux, F.; Neuville, R.; Hallot, P.; Van Wersch, L.; Jancsó, A.L.; Billen, R. Digital investigations of an archaeological smart point cloud: A real
time web-based platform to manage the visualisation of semantical queries. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS
Arch. 2017, XLII-5/W1, 581–588.
References
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Florent Poux - PC, Segments, Semantic and Automation
7. Poux, F.; Neuville, R.; Hallot, P.; Billen, R. MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA. ISPRS Ann. Photogramm. Remote Sens.
Spat. Inf. Sci. 2017, IV-4/W5, 107–115.
8. Poux, F.; Neuville, R.; Billen, R. POINT CLOUD CLASSIFICATION OF TESSERAE FROM TERRESTRIAL LASER DATA COMBINED WITH DENSE
IMAGE MATCHING FOR ARCHAEOLOGICAL INFORMATION EXTRACTION. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, IV-
2/W2, 203–211.
9. Poux, F.; Neuville, R.; Nys, G.-A.; Billen, R. 3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture.
Remote Sens. 2018, 10, 1412.
10. Poux, F.; Billen, R. A Smart Point Cloud Infrastructure for intelligent environments. In Laser scanning: an emerging technology in structural
engineering; Lindenbergh, R., Belen, R., Eds.; ISPRS Book Series; Taylor & Francis Group/CRC Press: United States, 2019 ISBN in generation.
11. Poux, F.; Hallot, P.; Neuville, R.; Billen, R. SMART POINT CLOUD: DEFINITION AND REMAINING CHALLENGES. ISPRS Ann. Photogramm.
Remote Sens. Spat. Inf. Sci. 2016, IV-2/W1, 119–127.
12. Poux, F. Vers de nouvelles perspectives lasergrammétriques : optimisation et automatisation de la chaîne de production de modèles 3D
Florent Poux To cite this version : HAL Id : dumas-00941990. 2014.
13. Novel, C.; Keriven, R.; Poux, F.; Graindorge, P. Comparing Aerial Photogrammetry and 3D Laser Scanning Methods for Creating 3D Models of
Complex Objects. In Proceedings of the Capturing Reality Forum; Bentley Systems: Salzburg, 2015; p. 15.
References
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