High-frequency region enhanced photometric three-dimensional reconstruction method based on deep learning

A technology of deep learning and photometric stereo, applied in neural learning methods, 3D modeling, complex mathematical operations, etc., can solve problems such as fuzzy 3D reconstruction results and large errors in high-frequency areas, so as to improve task accuracy and 3D reconstruction accuracy , the effect of rich details

Active Publication Date: 2022-01-14
OCEAN UNIV OF CHINA
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Problems solved by technology

[0003] However, the existing photometric stereo method based on deep learning has a large error in the high-frequency areas of the object surface, such as wrinkles and edges.

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  • High-frequency region enhanced photometric three-dimensional reconstruction method based on deep learning
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  • High-frequency region enhanced photometric three-dimensional reconstruction method based on deep learning

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Embodiment Construction

[0045] Such as figure 1 , a high-frequency area-enhanced photometric three-dimensional reconstruction method based on deep learning, which is characterized in that it includes the following steps:

[0046] 1) Using the photometric stereo system, take several images of the object to be reconstructed:

[0047] The object to be reconstructed is photographed under the illumination of a single parallel white light source, and the center of the object to be reconstructed is taken as the origin of the coordinate axis to establish a Cartesian coordinate system. The position of the white light source is determined by the vector in the Cartesian coordinate system l = [ x,y,z ]express;

[0048] Change the position of the light source to obtain images under another light direction; usually at least 10 images under different light directions are required to be taken, denoted as m 1 , m 2 , ..., m j ， At the same time, the corresponding light source position is denoted as l 1...

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Abstract

A high-frequency region enhanced photometric three-dimensional reconstruction method based on deep learning comprises the steps of shooting a plurality of images of an object to be reconstructed by using a photometric three-dimensional system, and outputting accurate surface normal three-dimensional reconstruction by using a deep learning algorithm, designing a surface normal generation network to generate a surface normal of an object needing to be reconstructed from an image and illumination; enabling the attention weight generation network to generate an attention weight map of an object needing to be reconstructed from the image; processing the attention weight loss function pixel by pixel; and using the trained network to carry out surface normal reconstruction of the photometric stereo image. According to the method, the surface normal information and the high-frequency information are learned respectively through the proposed surface normal generation network and the attention weight generation network, training is carried out by utilizing the proposed attention weight loss, and the reconstruction precision of the surface of the high-frequency region such as the wrinkle edge can be improved. Compared with a previous traditional photometric stereo method, the method improves the three-dimensional reconstruction precision, especially the details of the surface of the object to be reconstructed.

Description

technical field [0001] The invention relates to a high-frequency area enhanced photometric three-dimensional reconstruction method based on deep learning, which belongs to the field of multi-degree three-dimensional reconstruction. Background technique [0002] The 3D reconstruction algorithm is a very important and basic problem in computer vision. The photometric stereo algorithm is a high-precision pixel-by-pixel 3D reconstruction method, which uses the grayscale change clues provided by images under different illumination directions to restore the normal direction of the object surface. Photometric stereo has an irreplaceable position in many high-precision 3D reconstruction tasks. For example, it has important application value in archaeological exploration, pipeline inspection, and fine seabed mapping. [0003] However, the existing photometric stereo method based on deep learning has a large error in the high-frequency areas of the object surface, such as wrinkles and...

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Application Information

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IPC IPC(8): G06T17/30G06N3/08G06F17/16
CPCG06T17/30G06N3/08G06F17/16
Inventor 举雅琨董军宇高峰
Owner OCEAN UNIV OF CHINA
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