Rapid QA of Forensics and Public Safety 3D Scanning Outputs

Accurately capturing accident, crime or crash scenes, and other scenes relevant from a Public Safety standpoint is of utmost importance for law enforcement or other government agencies. This tedious and error-prone work has tremendously benefitted from the adoption of laser scanners made by OEMs like Faro, Leica and others.

Deploying laser scanners to capture crime scenes and other relevant real world situations generates a complex point clouds which requires extensive post processing before the final models are constructed – a process that takes place offsite after scanning. Therefore, potential errors of over or under scanning – which can significantly deteriorate the quality of the model – is difficult to detect on location. This is of particular concern when the use of scanning equipment is not frequent, e.g. a small town police department or large-scale disaster event.

By deploying AR glasses it is possible to track the location of the scanning device and combine it with its effective scanning range at every stage to create a visual representation of the scanning footprint. This could yield an effective “coverage map” visible via smart glasses that would help the operator spot under-scanned areas. This topic includes a detailed examination of scanning footprint detected via smart glasses, in comparison to the 3D scene captured by the actual point cloud.

Stakeholders

Public safety agencies such as police crime scene investigators, law enforcement, FEMA, OEM manufacturers. If techniques are widely published, Independent Software Vendors could integrate the capability as a new feature in their existing applications and support more devices.

Possible Methodologies

This research project will make a statistically-significant number of comparisons between point clouds and their respective scanner location tracking information of the same site with the purpose of creating a regression model that accounts for measurement errors in the independent variables.

Research Program

This research topic can be combined with other projects examining the tradeoffs between fidelity and function of large-scale 3D models.

Miscellaneous Notes

There’s an ISMAR 2019 paper about how to use AR device to track the viewpoint of the scanner to ensure that an object is fully scanned in 3D. This research topic could be an extension to the prior published research.

Keywords

Point cloud, laser scanning, disaster scene, crime scene, law enforcement, 3d models, point clouds, digital twin

Research Agenda Categories

Technology, Business

Expected Impact Timeframe

Medium

Related Publications

Using the words in this topic description and Natural Language Processing analysis of publications in the AREA FindAR database, the references below have the highest number of matches with this topic:

• Wei, Y., Zhang, T., Gu, K., & Shi, Z. (2017). Robust parameter estimation from point cloud data with noises for augmented reality. 2017 36th Chinese Control Conference (CCC).
• Dorit Borrmann; Andreas Nuechter; Thomas Wiemann .. Large-Scale 3D Point Cloud Processing for Mixed and Augmented Reality. (2018). 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).
• Li, D., Zhang, H., Song, Z., Man, D., & Jones, M. W. (2017). An automatic laser scanning system for accurate 3D reconstruction of indoor scenes. 2017 IEEE International Conference on Information and Automation (ICIA).
• Fakour Sevom, V., Schwarz, S., & Gabbouj, M. (2018). Geometry-Guided 3D Data Interpolation for Projection-Based Dynamic Point Cloud Coding. 2018 7th European Workshop on Visual Information Processing (EUVIP).
• De Oliveira Rente, P., Brites, C., Ascenso, J., & Pereira, F. (2019). Graph-Based Static 3D Point Clouds Geometry Coding. IEEE Transactions on Multimedia, 21(2), 284-299.

More publications can be explored using the AREA FindAR research tool.

Author

Peter Orban

Last Published (yyyy-mm-dd)

2021-08-31

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