Development of multi-view 3D reconstruction system for bubble flow measurement

This work introduces the development of bubble measurement method utilizing a three-dimensional (3D) reconstruction technique from multi-view images. Multiple synchronized cameras were positioned around a water container, and calibration was performed to obtain external and internal camera parameters. Images of bubbles emerging from a nozzle were captured, and a machine learning technique was used to extract bubble silhouettes as foreground probability distributions, enabling the extraction of bubbles from images obtained with a simple lighting setup. These distributions were then projected onto a 3D voxel space using the visual hull method. It was confirmed that the method can successfully capture bubble generation, detachment, and rise behaviors, offering insights into understanding bubble dynamics and potential applications in 3D computational fluid dynamics validation. • Development of a bubble measurement method using 3D reconstruction from multi-view images. • The method utilizes machine learning and modified Visual Hull, for flexible setups and wide applicability. • Air bubbles emerging from a nozzle at flow rates 1.0 to 10.0 SLPM were reconstructed at 120 frames per second.