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

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Presentation transcript:

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

Sea ice IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Overview Introduction Need for reconstruction Previous approaches Camera system and field trip Stereo on ice images Our algorithm Results Conclusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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/A 76 Projects, Final Report, pp 73. IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Camera system IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Field trip IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Samples IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Features in data Smoothly changing disparity No edge Low color variation IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Features in data Specular Highlights IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Stereo Disparity (d) Edge based matching(c) Non-Linear Diffusion(b) Membrane Diffusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 1 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 10 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 20 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 50 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 80 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 120 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 150 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 200 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 250 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Diffusion 300 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Classification Unambiguous Low Variance Occluded IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Algorithm for Classification IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

How to fill Low Variance areas? Don’t have any unambiguous information about the depth at those pixels Interpolate from Boundary True Map Surface IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Interpolation 63 Sampled VerticesTrue Map IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

How to Interpolate? Given n points on the boundary Triangulate… Which Triangulation? Delaunay Triangulation True Map 61 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Subdivide Loop Subdivision True Map 244 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Subdivide True Map 3904 faces 976 faces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

What if…? True Map 104 faces 225 faces 425 faces 244 faces subdivision IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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… IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Unstructured Triangulation Algorithm IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Advantages Very simple Quality of Triangles is high Errors in Interpolation are low Can handle concave shapes and regions with holes IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Negatives Uses Delaunay to triangulate every iteration May become unstable with wrong choice of parameters (very rare) May not converge IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Finite Element Method Courtesy : A Pragmatic Introduction to the Finite Element Method for Thermal and Stress Analysis, Petr Krysl IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Finite Element Method Courtesy : IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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 triangulation From [1] Interpolation with Unstructured triangulation IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Result Ambiguous Unambiguous disparity Triangulation Final disparity IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Comparison (c) Non-Linear Diffusion (b) Membrane Diffusion IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion (e) Ground Truth

More results IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

More results IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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 IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

Future Directions Include disparity variance in factors for classification Change the differential equation to model developable surfaces IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion

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

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

Acknowledgements Dr. Chandra Kambhamettu Dr. Cathleen Geiger This work was made possible by National Science Foundation (NSF) Office of Polar Program grants, ANT and ARC IntroductionStereo on Ice ImagesOur AlgorithmResultsConclusion