1. http://www.nshorter.com 1 Clustering Irregular Spaced LiDAR TINs for 3D Reconstruction Nicholas S. Shorter 1 nshorter@mail.ucf.edu http://www.nshorter.com University of Central Florida 1 Orlando, Florida, 32816 USA Takis Kasparis 1 kasparis@mail.ucf.edu Michael Georgiopoulos 1 michaelg@mail.ucf.edu Georgios C. Anagnostopoulos 2 georgio@fit.edu Florida Institute of Technology 2 Melbourne, Florida, 32901, USA

2. http://www.nshorter.com2 Objectives – Project Overall Scope 3D Reconstruction from Aerial Imagery and LiDAR data Solely Concentrating on Buildings –Not reconstructing Trees, Cars, Power Lines, Roads, etc. Automate process as much as possible (minimal to no user intervention) End result to appear as 3D Models with images mapped to the models

3. http://www.nshorter.com3 LIDAR Overview Data Collection –Plane Equipped with GPS, INS & LIDAR –LIDAR – Light Detection and Ranging (active sensor) LiDAR sensor works day or night, cloud coverage or not Collection of 3D points Laser sent out from Emitter, reflects off of Terrain, Returns to Receiver –Receiver measures back scattered electromagnetic radiation (laser intensity) Time Difference Determines Range to Target http://www.toposys.com/

4. http://www.nshorter.com4 Sampling the following scene…

5. http://www.nshorter.com5 Raw Elevation Plot

6. http://www.nshorter.com6 Angled, Zoomed In View

7. http://www.nshorter.com7 Triangulated, Angled, Zoomed In View

8. http://www.nshorter.com8 Applications for 3D Reconstruction Military Applications –Automatic Target Recognition Commercial –Change Detection (Natural Disasters) –Network Planning for Mobile Communication –Noise Nuisance (Universal Studios, 408 Expressway - walls) –Urban Planning

9. http://www.nshorter.com9 Objectives – Conference Focus Comparing performance of Fuzzy ART, Gaussian ART and Fuzzy SART for unsupervised planar detection and reconstruction in building regions Proposing preprocessing techniques to improve detection performance

10. http://www.nshorter.com10 Triangulated Irregular Networks (TIN) Advantages of TIN –Generation Fully Automatic –Space Uniquely Defined –Cells Indexed Spatial Searches, Triangle Elevation Differences Disadvantages of TIN –Noise Causes Triangle Normal Vectors to deviate from Ideal

11. http://www.nshorter.com11 Greedy Insertion Triangulation Filtering Greedy Insertion Triangulation inserts points farthest away from the existing TIN Last points inserted typically belong to already established plane Following restrictions implemented:

12. http://www.nshorter.com12 Pitch & Roof Threshold Triangle Differences Used to Determine Roof Threshold:

13. http://www.nshorter.com13 Unsupervised Clustering Algorithm Comparison Baraldi and Parmiggiani’s Fuzzy SART –Uses Hyperspherical Arcs as pattern encoding –Long term memory templates correspond to building plane’s ideal normal vector direction Williamson’s Gaussian ART –Gaussian distributions to encode patterns –Algorithm is dependent on the amount of noise present rather than how noise is distributed Carpenter and Grossberg’s Fuzzy ART –Hyper-Rectangles as pattern encoding

14. http://www.nshorter.com14 Input Preprocessing Cartesian Normal Vectors for Triangles converted to polar coordinates for all algorithms considered Fuzzy SART and Gaussian ART –Normalized inputs to single standard deviation Fuzzy ART –Inputs were compliment encoded Buildings manually extracted and then presented to algorithms

15. http://www.nshorter.com15 Performance Metrics - (CPM) Category Proliferation Measure (CPM) –Histogram created of all cluster label occurrences Arranged from greatest to least based on occurrences –Total number of roof triangles for building – Tr –Total number of roof planes (a-priori) - Pr –The top largest clusters composing about 50% of the total number of Tr for building called significant clusters, abbreviated Cr Cr >= Pr –If top 2 encode 45% but top 3 encode 60%, then top 3 are chosen –However, if top encodes 60% but Pr = 2, then top 2 are chosen

16. http://www.nshorter.com16 Performance Metrics - (CPM) Triangles with cluster labels not belonging to Cr’s are noise triangles – Tn Planar regression creates best fit plane from largest clusters –Works best when single cluster has 80% of its members belonging to correct plane CPM evaluates algorithm’s ability to encode roof planes with fewest categories

17. http://www.nshorter.com17 Performance Metrics - (CPM) CPM Equation: Ideally: –All triangles belong to Cr, therefore Tn = 0 and Pr = Cr and CPM = 0 Poor Performance –Lots of noise triangles (Tn > 0), ratio (Tr-Tn)/Tr decreases, more Cr clusters than Pr and Pr/Cr ratio decreases, CPM -> 1

18. http://www.nshorter.com18 Performance Metrics - (ECM) If Cr has 80% of total triangles belonging to single roof plane (no more than 20% to mult.) –Triangles belonging to other roof planes = Ti If Cr has less than 80% of total triangles belonging to single roof plane –All triangles = Ti Encoding Correctness Measure

19. http://www.nshorter.com19 Performance Metrics - (ECM) Ideally –Ti = 0, ECM = 100% Poor Performance –Ti >> 0, ECM  0% –Number of clusters correctly encoding planes is small –Success rate for 3D reconstruction from planar regression is poor

20. http://www.nshorter.com20 Algorithm Parameter Settings Best performance determined, then parameters were fixed for all buildings –Ideally parameters shouldn’t be changed; impractical to have to adjust for each building FSART terminated after 5 epochs; FA for 50 epochs, GA for 10 epochs

21. http://www.nshorter.com21 Data Set Specifications 3 Commercial Buildings Tested with both LiDAR data and Aerial Image coverage LiDAR data has 1.3m^2 point density Aerial Image has 15 centimeter pixel resolution Data covers urban and residential scene in Fairfield Australia

22. http://www.nshorter.com22 Aerial Photographs

23. http://www.nshorter.com23 CPM and ECM for Building 1

24. http://www.nshorter.com24 CPM and ECM for Building 2

25. http://www.nshorter.com25 CPM and ECM for Building 3

26. http://www.nshorter.com26 Conclusions Proposed preprocessing techniques show improvements for algorithms’ performance Fuzzy SART’s activation function enables the algorithm to have better performance of those compared –Long term weights corresponding to plane normal vector direction –Hyper-spherical pattern encoding mechanism yields best results

27. http://www.nshorter.com27 FSART – Building 1

28. http://www.nshorter.com28 FSART – Building 2

29. http://www.nshorter.com29 FSART – Building 3

30. http://www.nshorter.com30 Future Work Algorithm needs to be tested on additional buildings of varying complexity Other algorithms should be included for study – ISODATA, Ellipsoidal ART, etc. Investigation of algorithm custom tailored for reconstruction should be considered (instead of general purpose)

31. http://www.nshorter.com31 Acknowledgements Funding From Harris Cooperation Fairfield Data Set from Dr. Simone Clode, Dr. Franz Rottensteiner, AAMHatch Advisor (Committee Chair) –Dr. Takis Kasparis Committee Members –Dr. Georgios Anagnostopoulos, Dr. Michael Georgiopoulos, Dr. Andy Lee, Dr. Abhijit Mahalanobis, and Dr. Wasfy Mikhael