  We present a feature-free photogrammetric technique that enables quantitative
3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy
from sequences of images acquired by a smartphone at close range (several cm)
under freehand motion without additional hardware. Our end-to-end,
pixel-intensity-based approach jointly registers and stitches all the images by
estimating a coaligned height map, which acts as a pixel-wise radial
deformation field that orthorectifies each camera image to allow homographic
registration. The height maps themselves are reparameterized as the output of
an untrained encoder-decoder convolutional neural network (CNN) with the raw
camera images as the input, which effectively removes many reconstruction
artifacts. Our method also jointly estimates both the camera's dynamic 6D pose
and its distortion using a nonparametric model, the latter of which is
especially important in mesoscopic applications when using cameras not designed
for imaging at short working distances, such as smartphone cameras. We also
propose strategies for reducing computation time and memory, applicable to
other multi-frame registration problems. Finally, we demonstrate our method
using sequences of multi-megapixel images captured by an unstabilized
smartphone on a variety of samples (e.g., painting brushstrokes, circuit board,
seeds).
