Post on 17-Jan-2016
Surface reconstruction of sea-ice through stereo - initial steps
Rohith MVGowri Somanath
VIMS Lab
Sea iceIntroduction 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
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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
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
Camera systemIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
Field tripIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
SamplesIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Features in data
Smoothly changing disparityNo edge Low color variation
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Features in data
Specular Highlights
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Stereo Disparity
(d) Edge based matching(c) Non-Linear Diffusion(b) Membrane Diffusion
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
1
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
10
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
20
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
50
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
80
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
120
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
150
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
200
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
250
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Diffusion
300
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Classification
Unambiguous Low Variance
Occluded
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Algorithm for ClassificationIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
How to fill Low Variance areas?
• Don’t have any unambiguous information about the depth at those pixels
• Interpolate from Boundary
True MapSurface
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Interpolation
63 Sampled Vertices True Map
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
How to Interpolate?
• Given n points on the boundary• Triangulate…
• Which Triangulation?• Delaunay Triangulation
True Map
61 faces
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Subdivide
• Loop SubdivisionTrue Map
244 faces
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Subdivide
True Map
3904 faces
976 faces
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
What if…?
True Map
104 faces
225 faces
425 faces
244 facessubdivision
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…
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Unstructured Triangulation Algorithm
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
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
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
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
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Finite Element Method
Courtesy :http://cfdlab.ae.utexas.edu/~roystgnr/libmesh_intro.pdf
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Result
Ambiguous Unambiguous disparity
Triangulation Final disparity
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
Comparison
(c) Non-Linear Diffusion
(b) Membrane Diffusion
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
(e) Ground Truth
More resultsIntroduction Stereo on Ice Images Our Algorithm Results Conclusion
More resultsIntroduction 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
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
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.
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..
Introduction Stereo on Ice Images Our Algorithm Results Conclusion
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