Optical building target three-dimensional reconstruction method based on deep neural network

A deep neural network and three-dimensional reconstruction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to improve accuracy and ensure quality

Active Publication Date: 2021-07-23
电子科技大学成都学院
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Problems solved by technology

[0002] With the development of modern technology, architectural 3D reconstruction technology has attracted more and more attention in today's society. People use computers to convert 2D images into 3D data. 3D data refers to the generated 3D images stored in the form of digital models in the computer However, traditional architecture can no longer meet people's needs, and 3D architecture plays an increasingly important role in neighborhoods or scenes such as urban planning, intelligent buildings, disaster monitoring, and digital cities.

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  • Optical building target three-dimensional reconstruction method based on deep neural network
  • Optical building target three-dimensional reconstruction method based on deep neural network
  • Optical building target three-dimensional reconstruction method based on deep neural network

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Embodiment Construction

[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] 1. Locally recursive optical image building segmentation based on OSTU criterion and image entropy

[0049] 1. Optical image preprocessing

[0050] The quality of the image processing effect often has a direct impact on the accurate segmentation rate and effect of the image. Image segmentation preprocessing usually needs to be completed before an image starts to be segmented. Image information preprocessing technology broadly refers to image recognition for each important part of the image input by the user, eliminates useless information in the input image information, restores the real and useful image information in the input image in time, and enhances the accuracy of the input image information. Accuracy, reliability and detection, and at the same time, the image data segmentation can be optimized and simplified, thereby...

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Abstract

The invention provides an optical building target three-dimensional reconstruction method based on a deep neural network, and the method comprises the steps of constructing an optical image building data set through marking, and constructing the deep neural network for extracting an optical building; preprocessing an input image to enhance the quality of an optical image, segmenting the processed optical image by using a local recursion method based on an OTSU criterion and image entropy, and training a data set of the processed optical image to enable a deep neural network to have better extraction capability; using a method for quickly simulating optical target light-target three-dimensional point cloud to establish a data set through SENSOR simulation software, and using a 3D-R2N2 network to perform centralized learning on obtained optical target image three-dimensional data, so that a two-dimensional image is mapped to a three-dimensional model, an instance of an object is obtained in an end-to-end manner, a three-dimensional reconstruction result of the optical image is obtained, and a good value is provided for the development of the building field.

Description

technical field [0001] The invention relates to the technical field of three-dimensional reconstruction, in particular to a method for three-dimensional reconstruction of optical architectural objects based on a deep neural network. Background technique [0002] With the development of modern technology, architectural 3D reconstruction technology has attracted more and more attention in today's society. People use computers to convert 2D images into 3D data. 3D data refers to the generated 3D images stored in the form of digital models in the computer However, traditional architecture can no longer meet people's needs, and 3D architecture is playing an increasingly important role in neighborhoods or scenes such as urban planning, intelligent buildings, disaster monitoring, and digital cities. Up to now, in-depth research has been carried out at home and abroad on the three-dimensional feature signal detection, feature signal matching, and camera feature calibration of buildi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/00G06T7/10G06N3/04G06N3/08
CPCG06T17/00G06T7/10G06N3/08G06T2207/20081G06T2207/20084G06N3/045
Inventor 邹倩颖郭雪喻淋蔡雨静
Owner 电子科技大学成都学院
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