[2211.11836] Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2211.11836 (eess)
[Submitted on 21 Nov 2022]

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

Authors:David Ramirez, Suren Jayasuriya, Andreas Spanias
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Abstract: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.
Comments: Accepted to 2022 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), 13 pages
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2211.11836 [eess.IV]
  (or arXiv:2211.11836v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2211.11836

Submission history

From: David Ramirez [view email]
[v1] Mon, 21 Nov 2022 19:57:51 UTC (5,978 KB)
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