Postcalibrating RBLFs Vaibhav Vaish

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

Acquiring Seven Slabs

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

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

Feature Correspondences

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

Estimating Camera-Gantry Pose s i R i R T x i – s j R j R T x j = [1 0 0 ] T Given images of a point in a row of the light field, we can estimate pose from the above equation.

Epipolar Geometry

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.

Bundle Adjustment: Results

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

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

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