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A Phase Resolution Method Based on Multi-Scale Attention Mechanism Network
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An analysis method and attention technology, applied in neural learning methods, biological neural network models, image analysis, etc., can solve the problems of weak textures rarely reported, lack of phase unwrapping methods, etc., to save the number of projected images, meet high-speed and high-speed Accurate 3D imaging, suppressing the effects of error diffusion and accumulation
Active Publication Date: 2022-06-07
SICHUAN UNIV
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In addition, these methods are mostly based on simulation data or biomedical data for continuous phase prediction, but there are few reports on deep learning phase analysis of real faces with weak textures.
Therefore, there is still a lack of a phase unwrapping method with both measurement accuracy and measurement efficiency for the situation where the surface shape of the measured target is relatively complex and the phase information changes drastically, especially in the case of spatial isolation, noise, shadow and undersampling areas.
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Embodiment 1
[0049] figure 1 A schematic flowchart of a phase resolution method based on a multi-scale attention mechanism network is shown, and the method includes the following steps:
[0050] S1 , project a three-frequency N-step phase-shifted fringe image to the object to be measured, and the camera synchronously captures the deformed fringe image modulated by the surface shape of the object to be measured.
[0051] The light intensity map of any set of N-step (N≥3) phase-shifted grating images of the projector can be expressed as:
[0052]
[0053] In formula (1), i=1,...,N represents the i-th phase shift (N≥3). I i (x, y) represents the light intensity function on the time axis, a(x, y) represents the background light intensity, and b(x, y) represents the modulation depth of the fringes. f 0 is the carrier frequency, Represents the modulated phase of this target point caused by the height distribution in the ith fringe pattern.
[0054] S2, based on the phase shift method, ...
Embodiment 2
[0083] In order to verify the phase unwrapping method provided by the embodiment of the present invention, a specific experimental case of the method is exemplarily given below.
[0084] figure 2 A schematic diagram of an experimental case of a complex surface reconstruction system based on binocular vision according to the invention is shown. The experimental conditions are set as follows:
[0085] (1) 2 completely isolated test targets: including 1 mask and 1 face model. (2) Projector: The frame rate is 120fps, the resolution is 1280*1024, each 12 structured light images are grouped, and the continuous loop projection, synchronously provides external trigger signals to the camera; (3) Camera: including the left camera 715 and the right camera The camera 716, the frame rate is 120fps, the resolution is 1280*1024, it receives the external trigger signal of the projector and shoots 2 measured targets illuminated by structured light, the projector and the left and right camera...
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Abstract
The present invention relates to the field of optical three-dimensional measurement, in particular to a phase analysis method based on a multi-scale attention mechanism network, comprising the following steps: S1 projects a three-frequency N-step phase shift fringe image to the measured object, and the camera synchronously shoots The deformed fringe image modulated by the surface shape of the object to be measured; S2 converts the deformed fringe image into truncated phase images of different frequencies based on the phase shift method; S3 extracts the truncated phase image of the highest frequency from the truncated phase images of different frequencies, and Input the truncated phase map with the highest frequency into the pre-trained multi-scale attention mechanism network, and output the corresponding continuous phase map; S4 maps the continuous phase map to the three-dimensional surface depth to obtain the three-dimensional point cloud data of the measured object. Using the method of the present invention, the continuous phase can be quickly predicted by inputting the truncated phase image into the pre-trained network model, which can suppress error diffusion and accumulation, and is not affected by the actual phase discontinuity, wearing glasses and other areas.
Description
technical field [0001] The present invention relates to the field of optical three-dimensional measurement, in particular to an efficient phase analysis method based on a multi-scale attention mechanism network. Background technique [0002] In recent years, 3D reconstruction has been a hot topic in computer vision. Many researches build 3D models by simulating the human binocular vision system, and use the parallax generated by observing the same object from left and right perspectives to obtain depth information combined with the principle of triangulation, which is widely used in machine tools. Intelligent, autonomous driving, industrial inspection, reverse engineering, virtual reality, 3D manufacturing and 3D printing and other fields. Although passive stereo matching has achieved good results, its 3D reconstruction effect is not ideal for targets containing large areas of weak texture and scenes with high modeling accuracy requirements. Therefore, it is necessary to ad...
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