A 3-D imaging technique is presented which pairs high-resolution night-vision cameras with GPS to increase the
capabilities of passive imaging surveillance. Camera models and GPS are used to derive a registered point cloud from
multiple night-vision images. These point clouds are used to generate 3-D scene models and extract real-world positions
of mission critical objects. Analysis shows accuracies rivaling laser scanning even in near-total darkness. The technique
has been tested on stereoscopic 3-D video collections as well. Because this technique does not rely on active laser
emissions it is more portable, less complex, less costly, and less detectable than laser scanning. This study investigates
close-range photogrammetry under night-vision lighting conditions using practical use-case examples of terrain
modeling, covert facility surveillance, and stand-off facial recognition. The examples serve as the context for discussion
of a standard processing workflow. Results include completed, geo-referenced 3-D models, assessments of related
accuracy and precision, and a discussion of future activities.
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