Computing Epipolar Geometry From Dynamic 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
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.
Results : Pair-wise Epipolar Geometry on datasets.Goal : To Calibrate a Network of Cameras from video streams.
Metric Camera Calibration from Fundamental (F) Matrices.
Motivation: No Calibration Object (LED, grid) required. Handles wide baselines, lack of sufficient scene overlap, lack of enough point features and arbitrary camera configuration.
Frontier Points and constraints provided by Epipolar Tangents. Compute Convex Hull + its Dual
Hypothesis Step
Verification Step
Incremental Projective Reconstruction
Given, F12 , F23 , F13
compute consistent projective camerasP1 , P2 , P3 Work in progress:
Silhouette Extraction
Compute Pair wise Epipolar Geometry using Silhouettes
Metric CalibrationOf Camera
Network From Fundamental
Matrices
F12..Fjk
P1P2..PnMultiple Video Streams
Funding: NSF Career IIS 0237533 & DARPA/DOI NBCH 1030015
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 addnew cameras to existing network.
The re-projected Visual Hull in one of the views
Temporal Interpolation of Silhouettes
MIT Dataset (4 views), 4 minutes of 30 fps video
Synthetic Data (MPI Saarbrucken) (25 views, 200 frames)
Presented at “Mathematical Methods in Computer Vision Workshop, Banff, Canada, Oct 2006
Name Cameras Frames Good F’s Projective Bundle Adj.
MIT 4 7000 5 / 6 1580 pts 0.26 pixels
INRIA 8 200 20 / 28 3243 pts 0.25 pixels
KUNGFU 25 200 268 / 300 19632 pts 0.11 pixels
GATECH 9 200 25 / 36 2503 pts 0.55 pixels
CMU 8 900 22 / 28 5764 pts 0.47 pixels
MPI 8 370 26 / 28 4975 pts 0.28 pixels
SriKumar 5 1000 9 / 10 1686 pts 0.24 pixels
Incremental Projective Reconstruction
+ Projective Bundle
Adjustment
Self-Calibration
Euclidean Bundle
Adjustment
F12..Fjk
P1P2..Pn
1. Projector Camera Calibration2. 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.
MIT
INRIA
GATECH
MPI
CMU
KUNGFU
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