
Proceedings Paper
"AFacet": a geometry based format and visualizer to support SAR and multisensor signature generationFormat | Member Price | Non-Member Price |
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Paper Abstract
When simulating multisensor signature data (including SAR, LIDAR, EO, IR, etc...), geometry data are required that accurately represent the target. Most vehicular targets can, in real life, exist in many possible configurations. Examples of these configurations might include a rotated turret, an open door, a missing roof rack, or a seat made of metal or wood. Previously we have used the Modelman (.mmp) format and tool to represent and manipulate our articulable models. Unfortunately Modelman is now an unsupported tool and an undocumented binary format. Some work has been done to reverse engineer a reader in Matlab so that the format could continue to be useful. This work was tedious and resulted in an incomplete conversion. In addition, the resulting articulable models could not be altered and re-saved in the Modelman format. The AFacet (.afacet) articulable facet file format is a replacement for the binary Modelman (.mmp) file format. There is a one-time straight forward path for conversion from Modelman to the AFacet format. It is a simple ASCII, comma separated, self-documenting format that is easily readable (and in many cases usefully editable) by a human with any text editor, preventing future obsolescence. In addition, because the format is simple, it is relatively easy for even the most novice programmer to create a program to read and write AFacet files in any language without any special libraries. This paper presents the AFacet format, as well as a suite of tools for creating, articulating, manipulating, viewing, and converting the 370+ (when this paper was written) models that have been converted to the AFacet format.
Paper Details
Date Published: 27 April 2018
PDF: 18 pages
Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 1064702 (27 April 2018); doi: 10.1117/12.2304460
Published in SPIE Proceedings Vol. 10647:
Algorithms for Synthetic Aperture Radar Imagery XXV
Edmund Zelnio; Frederick D. Garber, Editor(s)
PDF: 18 pages
Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 1064702 (27 April 2018); doi: 10.1117/12.2304460
Show Author Affiliations
Stephen Rosencrantz, Wright State Univ. (United States)
John Nehrbass, Wright State Univ. (United States)
John Nehrbass, Wright State Univ. (United States)
Ed Zelnio, Air Force Research Lab. (United States)
Beth Sudkamp, Air Force Research Lab. (United States)
Beth Sudkamp, Air Force Research Lab. (United States)
Published in SPIE Proceedings Vol. 10647:
Algorithms for Synthetic Aperture Radar Imagery XXV
Edmund Zelnio; Frederick D. Garber, Editor(s)
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