Gigapixel videography, beyond the resolution of single camera and human visual perception, plays an important role in capturing large-scale dynamic scene with extremely high resolution for both macro and micro domains. Restricted by the spatial-temporal bandwidth product of optical system, the size, weight, power and cost are central challenges in gigapixel video.
The UnstructuredCam we designed, an end-to-end unstructured multi-scale camera system, shows the ability of real-time capture, dynamically adjusting local-view cameras, and online warping for synthesizing gigapixel video. We take the advantage of our UnstructuredCam to develop the Tsinghua HumanNet Dataset. And these datasets we provide in www.gigacamera.com are all characterized by extremely high resolution, large scale and wide FOV. We hope that our datasets will help researchers explore the cutting-edge computer vision tasks such as long-term multi-target object tracking, large-scale crowd counting, large-scale generic object detection, etc.
Fig.1 Illustration of representative imaging systems. (a) single camera imaging system faces the contradiction between wide FOV and high resolution, (b) single-scale camera array imaging  relies on image stitching, (c) structured multi-scale camera array (AWARE2) adopts two-stage optical imaging design, (d) un-structured multi-scale camera array (denoted as UnstructuredCam).
Large-scale imaging of biological dynamics at high spatiotemporal resolution is indispensable in the study of system biology. However, with conventional microscopes, one has to make a compromise between large field-of-view (FOV) and high spatial resolution, resulting from the inherently limited space-bandwidth-product (SBP). In addition, no imaging system is of sufficient data throughputs to record such huge information yet. Here we break these bottlenecks by proposing the flat-curved-flat strategy, in which the sample plane is magnified into a large spherical image surface and then is seamlessly conjugated to multiple planar sensors with a relay lens array. Accordingly, we develop a customized objective of globally-uniform 0.92 μm resolution across a 10 mm×12 mm FOV, and an accompanying camera array for high-throughput recording at 5.1 giga-pixels per second. We demonstrate the first reported video-rate, giga-pixel imaging of biological dynamics at centimeter scale and micron resolution, including brain-wide structural imaging and functional imaging in awake, behaving mice.
Fig.2 Schematic and point spread function (PSF) of RUSH.
Live Demo of UnstructuredCam
Live Demo of RUSH Macroscopy: Brain-wide functional imaging in awake mice with music stimuli.
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