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

Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques