Download - Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab
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Surface reconstruction of sea-ice through stereo - initial steps
Rohith MVGowri Somanath
VIMS Lab
![Page 2: Surface reconstruction of sea-ice through stereo - initial steps Rohith MV Gowri Somanath VIMS Lab](https://reader036.vdocuments.net/reader036/viewer/2022062804/56649f175503460f94c2d75a/html5/thumbnails/2.jpg)
Sea iceIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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Overview
• Introduction• Need for reconstruction• Previous approaches• Camera system and field trip
• Stereo on ice images• Our algorithm• Results• Conclusion
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Need for reconstruction• “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
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Previous methods – LIDAR
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.
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Camera systemIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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Field tripIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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SamplesIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Features in data
Smoothly changing disparityNo edge Low color variation
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Features in data
Specular Highlights
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Stereo Disparity
(d) Edge based matching(c) Non-Linear Diffusion(b) Membrane Diffusion
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Diffusion
1
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Diffusion
10
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Diffusion
20
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Diffusion
50
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Diffusion
80
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Diffusion
120
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Diffusion
150
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Diffusion
200
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Diffusion
250
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Diffusion
300
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Classification
Unambiguous Low Variance
Occluded
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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Algorithm for ClassificationIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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How to fill Low Variance areas?
• Don’t have any unambiguous information about the depth at those pixels
• Interpolate from Boundary
True MapSurface
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Interpolation
63 Sampled Vertices True Map
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How to Interpolate?
• Given n points on the boundary• Triangulate…
• Which Triangulation?• Delaunay Triangulation
True Map
61 faces
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Subdivide
• Loop SubdivisionTrue Map
244 faces
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Subdivide
True Map
3904 faces
976 faces
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What if…?
True Map
104 faces
225 faces
425 faces
244 facessubdivision
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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…
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Unstructured Triangulation Algorithm
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Advantages
• Very simple• Quality of Triangles is high
• Errors in Interpolation are low• Can handle concave shapes
and regions with holes
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Negatives
• Uses Delaunay to triangulate every iteration
• May become unstable with wrong choice of parameters (very rare)
• May not converge
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Finite Element Method
Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl
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Finite Element Method
Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl
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Finite Element Method
Courtesy :http://cfdlab.ae.utexas.edu/~roystgnr/libmesh_intro.pdf
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True surface True map 63 samples on boundary
Interpolation with Delaunay
Delaunay Triangulation (61 faces) Delaunay + Loop Subdivision (244 faces)
Interpolation of Delaunay + Loop Subdivision
Unstructured triangulationFrom [1]
Interpolation with Unstructured triangulation
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Result
Ambiguous Unambiguous disparity
Triangulation Final disparity
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Comparison
(c) Non-Linear Diffusion
(b) Membrane Diffusion
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
(e) Ground Truth
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More resultsIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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More resultsIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
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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
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Future Directions
• Include disparity variance in factors for classification
• Change the differential equation to model developable surfaces
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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.
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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..
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Acknowledgements
• Dr. Chandra Kambhamettu• Dr. Cathleen GeigerThis work was made possible by National
Science Foundation (NSF) Office of Polar Program grants, ANT0636726 and ARC0612105.
Introduction Stereo on Ice Images Our Algorithm Results Conclusion