Postcalibrating RBLFs

1. Postcalibrating RBLFs Vaibhav Vaish

2. A “Really Big Light Field” • 1300x1030 color images • 62x56 viewpoints per slab • Seven slabs of 3472 images each • 24304 image light field, 96GB raw, 16GB after JPEG compression

3. Acquiring Seven Slabs

4. Finding Motion Between Slabs Problem: Compute the relative motion of the gantry between different slabs Algorithm: • Find feature correspondences within slabs • Reconstruct accurate geometry • Match geometry computed from adjacent slabs

5. Feature Detection Reconstruct Geometry Match Geometry Estimate Motion The Pipeline For Each Slab Find Correspondences Manual Input For Few Images Extend to Entire Slab

6. Feature Correspondences

7. Camera Pose wrt Gantry • Camera pose known in world frame • Camera motion known in gantry frame • Compute gantry to world, world to camera pose • Enforce planar motion constraint

9. Estimating Camera-Gantry Pose siRiRTxi – sjRjRTxj = [1 0 0 ]T • Given images of a point in a row of the light field, we can estimate pose from the above equation.

10. Epipolar Geometry

11. Bundle Adjustment Find 3D coordinates of a point which minimize the projection error in images • Initialize the minimization by stereo triangulation • Use nonlinear least squares (lsqnonlin) • Works well for images in a column, poorly for row of images.

12. Bundle Adjustment: Results

13. Feature Detection Reconstruct Geometry Match Geometry Estimate Motion The Pipeline: What Worked Find Correspondences Manual Input For Few Images Extend to Entire Slab

14. Feature Detection Reconstruct Geometry Match Geometry Estimate Motion … and what didn’t  Find Correspondences Manual Input For Few Images Extend to Entire Slab

15. Acknowledgements • Szymon Rusinkiewicz • Sean Anderson • Steve Marschner • Billy Chen • The Digital Michelangelo Team