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Seeing Double: Can We Reduce The Number Of Cameras In Tomo-PTV?

Fernando Zigunov (1), John Charonko (2)

1. Syracuse University, Syracuse, United States
2. Los Alamos National Laboratory, Los Alamos, United States


In this work, we explore the possibility of reducing the number of cameras for tomographic particle tracking velocimetry (PTV) by using beamsplitters to project multiple viewpoints on a single camera sensor. This optical train allows a trade-off between particle seeding density and number of cameras, which enables the reduction of camera cost at the highest end of high-speed tomographic PTV applications where camera cost is considerable. Results are presented with synthetically-generated images for unmodified versions of state-of-the-art PTV algorithms (OpenLPT and DaVis 10.1), and a modification is implemented to the OpenLPT algorithm to include the knowledge that the views are multiplied, enabling the removal of more particles at every iteration of the iterative particle reconstruction (IPR) step. We observe that incorporating the knowledge of the image-multiplied projection brings the performance of the OpenLPT PTV algorithm very close to the baseline performance for equivalent particle seeding density values. However, when using real doubled experimental images we note a major challenge when performing volume self-calibration. The volume self-calibration process is significantly affected by the image doubling, and the knowledge of image doubling has to also be incorporated into the volume self-calibration process, which is left as future work.

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