Computational 3D topographic microscopy from terabytes of data per
  sample
Kevin C. Zhou Mark Harfouche Maxwell Zheng Joakim Jönsson Kyung Chul Lee Ron Appel Paul Reamey Thomas Doman Veton Saliu Gregor Horstmeyer Roarke Horstmeyer
We present a large-scale computational 3D topographic microscope that enables
6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110
cm$^2$ areas over multi-millimeter axial ranges. Our computational microscope,
termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a
Computational Array Microscope), features a parallelized, 54-camera
architecture with 3-axis translation to capture, for each sample of interest, a
multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640
9.4-megapixel images. We developed a self-supervised neural network-based
algorithm for 3D reconstruction and stitching that jointly estimates an
all-in-focus photometric composite and 3D height map across the entire field of
view, using multi-view stereo information and image sharpness as a focal
metric. The memory-efficient, compressed differentiable representation offered
by the neural network effectively enables joint participation of the entire
multi-TB dataset during the reconstruction process. To demonstrate the broad
utility of our new computational microscope, we applied STARCAM to a variety of
decimeter-scale objects, with applications ranging from cultural heritage to
industrial inspection.