Journal
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
Volume 152, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijmultiphaseflow.2022.104100
Keywords
Bubbly flow; 3D shape reconstruction; Neural network; Image processing; Synthetic dataset
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Funding
- National Natural Science Foundation of China [11535009]
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This paper proposes a novel method for the 3D reconstruction of bubble shape based on single-view images. By utilizing grayscale information and neural networks, it can automatically reconstruct the rough 3D shapes of one side of bubbles in the images.
The study of bubbly flows relies on the extraction of bubble information in experiments. Extraction with image processing based on images taken by high-speed cameras is a commonly adopted approach. Current methods mostly deal with silhouettes, abandoning the grayscale information in the images. In this paper, we propose BubDepth, a workflow that utilizes grayscale information and automatically reconstructs rough 3D shapes of one side of the bubbles from single-view images. The workflow consists of two parts: segmentation and depth inference. A neural network is used to recognize bubbles and masks in the segmentation part. The following depth inference network computes a relative depth map for each mask, describing the 3D shapes of one side of bubbles. The neural networks are trained using a dataset generated by computer graphics techniques. The image generator can create synthetic images of scenes labeled with 3D shape information of bubbles. BubDepth is a novel method for the 3D reconstruction of bubble shape based on single-view images. It achieved accurate results for synthetic images and could produce convincing predictions in the tests for real images.
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