Surface reconstruction of sea-ice through stereo - initial steps

1. Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab

2. Sea ice Introduction Stereo on Ice Images Our Algorithm Results Conclusion

3. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Overview • Introduction • Need for reconstruction • Previous approaches • Camera system and field trip • Stereo on ice images • Our algorithm • Results • Conclusion

4. Need for reconstruction Introduction Stereo on Ice Images Our Algorithm Results Conclusion • “The feasibility of using snow surface roughness to infer ice thickness and ice bottom roughness is promising….” • “…the goal of a circumpolar high resolution data set of Antarctic sea ice and snow thickness distributions has not yet been achieved …” • “…crucial for future validation of satellite observations, climate models, and for assimilation into forecast models…” Ref: Workshop on Antarctic Sea Ice Thickness, 2006; Annals of Glaciology

5. Previous methods – LIDAR Introduction Stereo on Ice Images Our Algorithm Results Conclusion Echelmeyer, K.A., V.B. Valentine, and S.L. Zirnheld, (2002, updated 2004): Airborne surface profiling of Alaskan glaciers. Boulder, CO: National Snow and Ice Data Center. Digital media. Dalå, N. S., R. Forsberg, K. Keller, H. Skourup, L. Stenseng, S. M.Hvidegaard, (2004): Airborne LIDAR measurements of sea ice north of Greenland and Ellesmere Island 2004, GreenICe/SITHOS/CryoGreen/A76 Projects, Final Report, pp 73.

6. Camera system Introduction Stereo on Ice Images Our Algorithm Results Conclusion

7. Field trip Introduction Stereo on Ice Images Our Algorithm Results Conclusion

8. Samples Introduction Stereo on Ice Images Our Algorithm Results Conclusion

9. Introduction Stereo on Ice Images Our Algorithm Results Conclusion

10. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Features in data Smoothly changing disparity No edge Low color variation

11. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Features in data Specular Highlights

12. Introduction Stereo on Ice Images Our Algorithm Results Conclusion (b) Membrane Diffusion (c) Non-Linear Diffusion (d) Edge based matching Stereo Disparity

13. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 1

14. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 10

15. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 20

16. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 50

17. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 80

18. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 120

19. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 150

20. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 200

21. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 250

22. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Diffusion 300

23. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Classification Unambiguous Low Variance Occluded

24. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Algorithm for Classification

25. Introduction Stereo on Ice Images Our Algorithm Results Conclusion True Map Surface How to fill Low Variance areas? • Don’t have any unambiguous information about the depth at those pixels • Interpolate from Boundary

26. Introduction Stereo on Ice Images Our Algorithm Results Conclusion 63 Sampled Vertices True Map Interpolation

27. Introduction Stereo on Ice Images Our Algorithm Results Conclusion True Map How to Interpolate? • Given n points on the boundary • Triangulate… • Which Triangulation? • Delaunay Triangulation 61 faces

28. Introduction Stereo on Ice Images Our Algorithm Results Conclusion True Map Subdivide • Loop Subdivision 244 faces

29. Introduction Stereo on Ice Images Our Algorithm Results Conclusion True Map Subdivide 976 faces 3904 faces

30. Introduction Stereo on Ice Images Our Algorithm Results Conclusion True Map What if…? 104 faces 225 faces 244 faces subdivision 425 faces

31. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Towards Algorithm • Don’t know vertices…Don’t know edges • Given Vertices…What are the best edges? • Delaunay Triangulation • Outline • Scatter Points • Triangulate • Move Points • Repeat…

32. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Unstructured Triangulation Algorithm

33. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Advantages • Very simple • Quality of Triangles is high • Errors in Interpolation are low • Can handle concave shapes and regions with holes

34. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Negatives • Uses Delaunay to triangulate every iteration • May become unstable with wrong choice of parameters (very rare) • May not converge

35. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl

36. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl

37. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Finite Element Method Courtesy :http://cfdlab.ae.utexas.edu/~roystgnr/libmesh_intro.pdf

38. Introduction Stereo on Ice Images Our Algorithm Results Conclusion 63 samples on boundary True surface True map Unstructured triangulation From [1] Delaunay Triangulation (61 faces) Delaunay + Loop Subdivision (244 faces) Interpolation with Unstructured triangulation Interpolation with Delaunay Interpolation of Delaunay + Loop Subdivision

39. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Result Unambiguous disparity Ambiguous Triangulation Final disparity

40. Introduction Stereo on Ice Images Our Algorithm Results Conclusion (c) Non-Linear Diffusion (b) Membrane Diffusion Comparison (e) Ground Truth

41. Introduction Stereo on Ice Images Our Algorithm Results Conclusion More results

42. Introduction Stereo on Ice Images Our Algorithm Results Conclusion More results

43. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Conclusions • In areas containing very low color variation, interpolation gives better results than image matching • Heuristic for classifying image regions • Efficient methods for interpolation using triangulation and FEM

44. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Future Directions • Include disparity variance in factors for classification • Change the differential equation to model developable surfaces

45. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Publications • Towards Estimation of Dense Disparities from Stereo Images Containing Large Textureless Regions. Rohith MV, Gowri Somanath, Chandra Kambhamettu, Cathleen Geiger. 19th International Conference on Pattern Recognition. December 2008. Tampa, USA • Reconstruction Of Snow And Ice Surfaces Using Multiple View Vision Techniques. Gowri Somanath, Rohith MV, Cathleen Geiger, Chandra Kambhamettu. 65th Eastern Snow Conference, May 2008, Vermont, USA.

46. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Bibliography • Daniel Scharstein, Richard Szeliski. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. IJCV 2001. • D. Scharstein, R. Szeliski, Stereo matching with Non-linear Diffusion. Computer Science TR 96-1575, Cornell University, Mar 1996. • D. Scharstein, R. Szeliski. Stereo Matching with Non-linear diffusion. CVPR. June 1996. • Jochen Alberty, Carsten Carstensen, Stefan Funken, Remarks Around 50 Lines of MATLAB:Short Finite Element Implementation, Numerical Algorithms,Volume 20, 1999. • P. Persson, G.Strang. A simple mesh generator in Matlab. SIAM Review, Volume 46 (2), June 2004..

47. Introduction Stereo on Ice Images Our Algorithm Results Conclusion Acknowledgements • Dr. Chandra Kambhamettu • Dr. Cathleen Geiger This work was made possible by National Science Foundation (NSF) Office of Polar Program grants, ANT0636726 and ARC0612105.