Tomographic particle image velocimetry (PIV) is a recently developed method to measure three components of velocity within a volumetric space. We present a visual hull technique that automates identification and masking of discrete objects within the measurement volume, and we apply existing tomographic PIV reconstruction software to measure the velocity surrounding the objects. The technique is demonstrated by considering flow around falling bodies of different shape with Reynolds number ∼1,000. Acquired image sets are processed using separate routines to reconstruct both the volumetric mask around the object and the surrounding tracer particles. After particle reconstruction, the reconstructed object mask isusedto remove any ghost particles that otherwise appear within the object volume. Velocity vectors corresponding with fluid motion can then be determined up to the boundary of the visual hull without being contaminated or affected by the neighboring object velocity. Although the visual hull method isnot meant for precise tracking of objects, the reconstructed object volumes nevertheless can be used to estimate the object location and orientation at each time step.
Bibliographical noteFunding Information:
The authors gratefully acknowledge support from the National Science Foundation through Grant IDBR-0852875. D. Adhikari was partially supported by a University of Minnesota Graduate Fellowship while undertaking this research. The authors would like to thank the reviewers for their invaluable suggestions and comments.