Physical constraint-based residual network limited angle combustion field three-dimensional reconstruction method
A 3D reconstruction and limited-angle technology, applied in biological neural network models, neural learning methods, 3D modeling, etc., to achieve high reconstruction accuracy, improve robustness, and solve the effects of gradient explosion
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Embodiment 1
[0052] This implementation example will illustrate the three-dimensional reconstruction method of the limited-angle combustion field based on the residual network based on the physical constraints proposed by the present invention by performing numerical simulation reconstruction experiments on the flame model.
[0053] The device in this embodiment is composed of 12 CCD cameras arranged in an array, which can effectively save the space of the reconstruction device while performing multi-directional detection. Arrange 12 CCD cameras into three windows, each window has four CCD cameras. The 4 CCD cameras in each window form a window in a 2x2 arrangement, and there are 2 CCD cameras in each column, a total of 2 columns. The two CCD cameras in the same row are distributed along the height direction and fixed on the same fixed frame. The center of the CCD camera located below is parallel to the center of the flame. The distance between the two CCD cameras on the same fixed frame i...
Embodiment 2
[0075] This embodiment will illustrate the three-dimensional reconstruction method of the limited-angle combustion field based on the residual network proposed by the present invention through a real candle flame reconstruction experiment.
[0076] The reconstruction area where the burning candle flame is located is divided into 100 × 100 × 120 grids, and the actual size of each grid is 0.55mm.
[0077] Step 1: The device adopted in this embodiment is the same as the device with three windows and 12 CCD cameras in Embodiment 1. The CCD cameras with three windows and 12 different positions in the device collect 750 frames of continuous flame projection images, each frame It consists of 12 CCD cameras collecting a flame projection image at the same time; extract the position of the characteristic projection of flame emission intensity in 9000 flame projection images, and take the one-dimensional projection data of the same line of projection map corresponding to the position as i...
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