1. Next Generation 4-D Distributed Modeling and Visualization of Battlefield Next Generation 4-D Distributed Modeling and Visualization of Battlefield Avideh Zakhor UC Berkeley June, 2002

2. Participants yAvideh Zakhor, (UC Berkeley) yBill Ribarsky, (Georgia Tech) yUlrich Neumann (USC) yPramod Varshney (Syracuse) ySuresh Lodha (UC Santa Cruz)

3. Battlefield Visualization zGoal: Detailed, timely and accurate picture of the modern battlefield zMany sources of info to build “picture”: yArchival data, roadmaps, GIS and databases: static ySensor information from mobile agents at different times and location: dynamic. yMultiple modalities: fusion zHow to make sense of all these without information overload?

4. Major Challenges: Data zDisparate/conflicting sources zLarge volumes. zInherently uncertain: resulting models also uncertain. zNeed to be visualized on mobiles with limited capability. zTime varying, time dependent and dynamic.

5. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Fusion/ Decision Making UNCERTAINTY

6. Research Agenda zModel construction and update zSensor tracking and registration zReal time visualization and multi-model interaction zUncertainty processing and visualization: zFusion used in all of the above.

7. Visualization Pentagon Information Fusion Information Fusion Uncertainty Processing/ Visualization Uncertainty Processing/ Visualization 4D Modeling/ Update 4D Modeling/ Update Visualization Database Visualization Database Tracking/ Registration Tracking/ Registration

8. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Decision Making

9. Model Construction for Visualization zDevelop a framework for 3D model construction for urban areas: y Easy, fast, accurate, automatic y Compact to represent; y Easy to render; zStrategy: yFusion of multiple data sources: intensity, range, heading, speedometer, panoramic cameras. yIncorporate apriori models, e.g. digital roadmaps. yRegistration, tracking and calibration.

10. 3D Modeling: z Close-range modeling: y Ground based vehicle with multiple sensors z Far-range modeling: y Aerial/satellite imagery y Airborne Lidar data z Fusion of close range and far range info at multiple levels: y Data and models.

11. Combining Aerial and Ground Based Models Highly detailed model of street scenery & building façades 3D Model of terrain and building tops Complete 3D City Model Fusion Airborne Modeling Laser scans/images from plane Ground Based Modeling Laser scans & images from acquisition vehicle

12. Laser range scanners Digital roadmaps Aerial photos Ground Level Based Data Acquisition 2D laser scanners: horizontal and vertical Intensity camera (UCB) Hybrid DGPS Inertial sensors Cameras (USC)

13. Processing Ground Based Laser Data Histograms Segmentation Layer separation Interpolation

14. Resulting Models from Hole Filling Before hole filling After hole filling

15. Fusing Airborne model with ground based model Airborne Point cloud Ground based facade Merged airborne /façade model

16. 6 DOF Pose Estimation for texture mapping with 3 DOF pose with 6 DOF pose

17. Static Texture Mapping Copy texture of all triangles into “collage” image Typical texture reduction: factor 8 - 12

18. Aerial view of projected image texture (campus of Purdue University) Sensor Image plane View frustum Dynamic Texture Projection on LiDAR Data Enables Real Time, Multi Source Data Fusion Requires accurate 3D model, sensor model, and texture/model registration Tracking and registration algorithms

19. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Decision Making

20. Hierarchical, multiresolution methods for interactive visualization of extended, detailed urban Scenes Block Façade 1 … Façade N LOD Hierarchy … … … Object M … … … Object 1 … … … … … … … City- organized hierarchy

21. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Decision Making

22. Multimodal Interface to Augmented Reality Systems Speech and gesture multimodal interface test setup Multimodal interface in action Infrared lights Camera with Infrared filter Gesture pendant (worn on chest) Demonstration of use of gesture pendant to recognize hand gestures

23. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Decision Making UNCERTAINTY

24. Visualization of Uncertain Particle Movement zUncertainty in initial position, direction and speed zUncertainty modeled by Gaussian distribution

25. Modeling and Visualization of Uncertainty zSpatio-temporal GPS uncertainty models : yNumber of accessible/used satellites ySNR (Signal to Noise Ratio) yDOP (Dilution of Precision) zReal-time visualization of GPS-tracked objects and associated uncertainty within VGIS

26. Low Uncertainty Line Preserving Compression OriginalUnconstrained Coastline preserving

27. Hierarchical Line Simplification Intersection preserving simplification

28. Mobile AR Visualization Laser-------- Lidar-------- Radar------- Camera------ GPS--------- Maps-------- Gyroscope-- 3D model construction with texture Visualization Database Model update Mobiles with augmented reality sensors Fusion/ Decision Making

29. Bayesian Networks with Temporal Updates Information flow Objective: To incorporate time-dependence of observations and evidence in Bayesian inference networks.

30. Temporal Fusion in Multi-Sensor Target Tracking Systems For a multi-sensor tracking system, sensors can be either synchronous or asynchronous (temporally staggered) T: Sampling interval of synchronous sensors T1: Time difference between sensor 1 and sensor 2 in asynchronous- sensor case T=T1+T2

31. Distributed Sequential Detection: minimize time/bandwidth needed for detection is the amount of bandwidth (bits) assigned to sensor i=1,2,…,M Local Sensor #1 Local Sensor #2 Local Sensor #M Fusion Center

32. Transitions (1) yGovernment: xInteractions with AFRL, ONR, NASA, NIMA xPresentations to President Bush and Gov. Ridge xPresentations to program directors at STRICOM yIndustry: x Raytheon, Lockheed Martin, Boeing, Sarnoff x HJW, Sick, Bosch, Astech, Airborne 1 x Sensis, Andro computing solutions x Olympus x Rhythm and Hues Studio

33. Publications (1) z C. Früh and A. Zakhor, "3D model generation for cities using aerial photographs and ground level laser scans", Computer Vision and Pattern Recognition, Hawaii, USA, 2001, p. II-31-8, vol.2. z H. Foroosh, “ A closed-form solution for optical flow by imposing temporal constraints”, Proceedings 2001 International Conference on Image Processing, vol.3, pp.656-9. z C. Früh and A. Zakhor, "Data processing algorithms for generating textured 3D building façade meshes from laser scans and camera images”, accepted to 3D Data Processing, Visualization and Transmission, Padua, Italy, 2002 z John Flynn, “Motion from Structure: Robust Multi-Image, Multi-Object Pose Estimation”, Master’s thesis, Spring 2002, U.C. Berkeley z S. You, and U. Neumann. “Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration,” IEEE VR2001, pp.71-78, March 2001 z B. Jiang, U. Neumann, “Extendible Tracking by Line Auto-Calibration,” submitted to ISAR 2001 z J. W. Lee. “Large Motion Estimation for Omnidirectional Vision,” PhD thesis, University of Southern California, 2002

34. Publications (2) z J. W. Lee, B. Jiang, S. You, and U. Neumann. “Tracking with Vision for Outdoor Augmented Reality Systems,” submitted to IEEE Journal of Computer Graphics and Applications, special edition on tracking technologies, 2002 z William Ribarsky, “Towards the Visual Earth,” Workshop on Intersection of Geospatial and Information Technology, National Research Council (October, 2001). z William Ribarsky, Christopher Shaw, Zachary Wartell, and Nickolas Faust, “Building the Visual Earth,” to be published, SPIE 16 th International Conference on Aerospace/Defense Sensing, Simulation, and Controls (2002). z David Krum, William Ribarsky, Chris Shaw, Larry Hodges, and Nickolas Faust “Situational Visualization,” pp. 143-150, ACM VRST 2001 (2001). z David Krum, Olugbenga Omoteso, William Ribarsky, Thad Starner, and Larry Hodges “Speech and Gesture Multimodal Control of a Whole Earth 3D Virtual Environment,” to be published, Eurographics-IEEE Visualization Symposium 2002. Winner of SAIC Best Student Paper award. z William Ribarsky, Tony Wasilewski, and Nickolas Faust, “From Urban Terrain Models to Visible Cities,” to be published, IEEE CG&A. z David Krum, Olugbenga Omoteso, William Ribarsky, Thad Starner, and Larry Hodges “Evaluation of a Multimodal Interface for 3D Terrain Visualization,”submitted to IEEE Visualization 2002.

35. Publications (3) z Justin Jang, William Ribarsky, Chris Shaw, and Nickolas Faust, "View-Dependent Multiresolution Splatting of Non-Uniform Data," pp. 125-132, Eurographics-IEEE Visualization Symposium 2002 z C. K. Mohan, K. G. Mehrotra, and P. K. Varshney, ``Temporal Update Mechanisms for Decision Making with Aging Observations in Probabilistic Networks’’, Proc. AAAI Fall Symposium, Cape Cod, MA, Nov. 2001. z R. Niu, P. K. Varshney, K. G. Mehrotra and C. K. Mohan, `` Temporal Fusion in Multi-Sensor Target Tracking Systems’’, to appear in Proceedings of the Fifth International Conference on Information Fusion, July 2002, Annapolis, Maryland. z Q. Cheng, P. K. Varshney, K. G. Mehrotra and C. K. Mohan, ``Optimal Bandwidth Assignment for Distributed Sequential Detection’’, to appear in Proceedings of the Fifth International Conference on Information Fusion, July 2002, Annapolis, Maryland. zSuresh Lodha, Amin P. Charaniya, Nikolai M. Faaland, and Srikumar Ramalingam, "Visualization of Spatio-Temporal GPS Uncertainty within a GIS Environment" to appear in the Proceedings of SPIE Conference on Aerospace/Defense Sensing, Simulation, and Controls, April 2002. zSuresh K. Lodha, Nikolai M. Faaland, Amin P. Charaniya, Pramod Varshney, Kishan Mehrotra, and Chilukuri Mohan, "Uncertainty Visualization of Probabilistic Particle Movement", To appear in the Proceedings of The IASTED Conference on Computer Graphics and Imaging", August 2002.

36. Publications (4) zSuresh K. Lodha, Amin P. Charaniya, and Nikolai M.Faaland, "Visualization of GPS Uncertainty in a GIS Environment", Technical Report UCSC-CRP-02-22, University of California, Santa Cruz, April 2002, pages 1-100. zSuresh K. Lodha, Nikolai M. Faaland, Grant Wong, Amin Charaniya, Srikumar Ramalingam, and Arthur Keller, "Consistent Visualization and Querying of Geospatial Databases by a Location-Aware Mobile Agent", In Preparation, to be submitted to ACM GIS Conference, November 2002. zSuresh K. Lodha, Nikolai M. Faaland, and Jose Renteria, ``Hierarchical Topology Preserving Simplification of Vector Fields using Bintrees and Triangular Quadtrees'', Submitted for publication to IEEE Transactions on Visualization and Computer Graphics. zLilly Spirkovska and Suresh K. Lodha, ``AWE: Aviation Weather Data Visualization Environment'', Computers and Graphics, Volume 26, No.~1, February 2002, pp.~169--191. zSuresh K. Lodha, Krishna M. Roskin, and Jose Renteria, ``Hierarchical Topology Preserving Compression of 2D Terrains'', Submitted for publication to Computer Graphics Forum.

37. Publication (5) zSuresh K. Lodha and Arvind Verma, ``Spatio-Temporal Visualization of Urban Crimes on a GIS Grid'',Proceedings of the ACM GIS Conference, November 2000, ACM Press, pages 174--179. zChristopher Campbell, Michael Shafae, Suresh K. Lodha and D. Massaro, ``Multimodal Representations for the Exploration of Multidimensional Fuzzy Data", Submitted for publication to Behavior Research, Instruments, and Computers. zSuresh K. Lodha, Jose Renteria and Krishna M. Roskin, ``Topology Preserving Compression of 2D Vector Fields'', Proceedings of IEEE Visualization '2000, October 2000, pp. 343--350.

38. Cross Collaboration UCBUSCG.T.SYRUCSC Model const. & texture X X x Tracking & reg. & pose est. X X x Visualiza tion,rend ering x x X x Uncertain. processing x X x Uncertain. Visualizati on. x x X

39. Outline of Talks zA. Zakhor, U.C. Berkeley, "Overview" zC. Freuh, U.C. Berkeley, "Fast 3D model construction of urban environments" zU. Neuman, USC, "Tracking and Data Fusion for 4D Visualization" zBill Ribarsky, Georgia Tech, "4D Modeling and Mobile Visualization" z Lunch zPramod Varshney, Syracuse, "Temporal Uncertainty Computation, Fusion, and Visualization in Multisensor Environments" zS. Lodha, U.C. Santa Cruz, "Uncertainty Quantification and Visualization: Mobile Targets within Geo-Spatially Registered Terrains" zDiscussion, Feedback from Government