  Mixed reality (MR) is a key technology which promises to change the future of
warfare. An MR hybrid of physical outdoor environments and virtual military
training will enable engagements with long distance enemies, both real and
simulated. To enable this technology, a large-scale 3D model of a physical
environment must be maintained based on live sensor observations. 3D
reconstruction algorithms should utilize the low cost and pervasiveness of
video camera sensors, from both overhead and soldier-level perspectives.
Mapping speed and 3D quality can be balanced to enable live MR training in
dynamic environments. Given these requirements, we survey several 3D
reconstruction algorithms for large-scale mapping for military applications
given only live video. We measure 3D reconstruction performance from common
structure from motion, visual-SLAM, and photogrammetry techniques. This
includes the open source algorithms COLMAP, ORB-SLAM3, and NeRF using
Instant-NGP. We utilize the autonomous driving academic benchmark KITTI, which
includes both dashboard camera video and lidar produced 3D ground truth. With
the KITTI data, our primary contribution is a quantitative evaluation of 3D
reconstruction computational speed when considering live video.
