  To study the behavior of freely moving model organisms such as zebrafish
(Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it
would be ideal to use a light microscope that can resolve 3D information over a
wide field of view (FOV) at high speed and high spatial resolution. However, it
is challenging to design an optical instrument to achieve all of these
properties simultaneously. Existing techniques for large-FOV microscopic
imaging and for 3D image measurement typically require many sequential image
snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a
computational microscope based on a synchronized array of 54 cameras that can
capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to
230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second.
3D-RAPID features a 3D reconstruction algorithm that, for each synchronized
temporal snapshot, simultaneously fuses all 54 images seamlessly into a
globally-consistent composite that includes a coregistered 3D height map. The
self-supervised 3D reconstruction algorithm itself trains a
spatiotemporally-compressed convolutional neural network (CNN) that maps raw
photometric images to 3D topography, using stereo overlap redundancy and
ray-propagation physics as the only supervision mechanism. As a result, our
end-to-end 3D reconstruction algorithm is robust to generalization errors and
scales to arbitrarily long videos from arbitrarily sized camera arrays. The
scalable hardware and software design of 3D-RAPID addresses a longstanding
problem in the field of behavioral imaging, enabling parallelized 3D
observation of large collections of freely moving organisms at high
spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex
barbatus), fruit flies, and zebrafish larvae.
