  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.
