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Computing Epipolar Geometry From Dynamic Silhouettes

P 1 P 2 . . P n. Metric Calibration Of Camera Network From Fundamental Matrices. F 12 . . F jk. Compute Pair wise Epipolar Geometry using Silhouettes. Silhouette Extraction. Multiple Video Streams. MIT Dataset (4 views), 4 minutes of 30 fps video.

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Computing Epipolar Geometry From Dynamic Silhouettes

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  1. P1 P2 . . Pn Metric Calibration Of Camera Network From Fundamental Matrices F12 . . Fjk Compute Pair wise Epipolar Geometry using Silhouettes Silhouette Extraction Multiple Video Streams MIT Dataset (4 views), 4 minutes of 30 fps video The re-projected Visual Hull in one of the views P1 P2 . . Pn Incremental Projective Reconstruction + Projective Bundle Adjustment F12 . . Fjk Euclidean Bundle Adjustment Self- Calibration Temporal Interpolation of Silhouettes Synchronization and Calibration of a Camera Network for 3D Event Reconstruction from Live Video Sudipta N. Sinha Marc Pollefeys Department of Computer Science, University of North Carolina at Chapel Hill Results : Pair-wise Epipolar Geometry on datasets. Goal : To Calibrate a Network of Cameras from video streams. MPI INRIA Motivation: No Calibration Object (LED, grid) required. Handles wide baselines, lack of sufficient scene overlap, lack of enough point features and arbitrary camera configuration. Computing Epipolar Geometry From Dynamic Silhouettes Synthetic Data (MPI Saarbrucken) (25 views, 200 frames) KUNGFU CMU MIT Frontier Points and constraints provided by Epipolar Tangents. Compute Convex Hull + its Dual RANSAC-based Algorithm If the epipoles are known, then 3 pair of matching lines through the epipoles are required to compute the epipolar homography. RANSAC is used to randomly explore the 4D space of epipole hypothesis as well as robustly deal with incorrect silhouettes. GATECH Metric Camera Calibration from Fundamental (F) Matrices. Hypothesis Step Incremental Projective Reconstruction Given, F12 ,F23 , F13 compute consistent projective cameras P1 , P2 , P3 Work in progress: Verification Step 1. Projector Camera Calibration 2. Calibrating Hybrid Camera Networks Color, Range scanner, Depth sensor and Infra-red cameras. 3. Modify algorithm to work with fuzzy silhouettes to deal with gross silhouette extraction errors. Recovering Synchronization (Assume fixed known frame-rate) Add a random guess for temporal offset to the hypothesis. First, use slow-moving silhouettes for coarse sequence alignment and then use fast moving ones for recovering finer synchronization + epipolar geometry. Method of Induction used to add new cameras to existing network. Funding: NSF Career IIS 0237533 & DARPA/DOI NBCH 1030015 Presented at “Mathematical Methods in Computer Vision Workshop, Banff, Canada, Oct 2006

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