Photometric Stereo 3D Reconstruction Method Based on Deep Learning for High Frequency Region Enhancement

Abstract

The photometric stereo 3D reconstruction method based on high-frequency area enhancement based on deep learning, including using photometric stereo system to take several images of the object to be reconstructed, and using deep learning algorithm to output accurate 3D reconstruction of surface normal, including the surface normal generation network is It is designed to generate the surface normal of the object to be reconstructed from the image and lighting; the attention weight generation network generates the attention weight map of the object to be reconstructed from the image; the attention weight loss function is processed pixel by pixel; then the trained network is used Surface normal reconstruction from photometric stereo images. The present invention learns surface normal and high-frequency information respectively through the proposed surface normal generation network and attention weight generation network, and uses the proposed attention weight loss for training, which can improve the reconstruction accuracy of the surface in high-frequency areas such as fold edges . Compared with the previous traditional photometric stereo method, the accuracy of 3D reconstruction is improved, 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...

AI Technical Summary

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. The existing methods will generate blurred 3D reconstruction results in these areas. However, these areas are the focus and focus. where accurate reconstruction is required

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