A Deep Learning Architecture for 3D Mapping Urban Landscapes

In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead of the typical convolutional layers, some diffe...

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Bibliographic Details
Published in:Applied sciences 2021-12, Vol.11 (23), p.11551
Main Authors: Rodríguez-Santiago, Armando Levid, Arias-Aguilar, José Aníbal, Takemura, Hiroshi, Petrilli-Barceló, Alberto Elías
Format: Article
Language:English
Subjects:
3D reconstruction
autoencoder
Cameras
CNN
Computer architecture
Deep learning
Feature maps
GAN
Image retrieval
Localization
Neural networks
point cloud
Proposals
Sensors
Software
Urban environments
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Summary:In this paper, an approach through a Deep Learning architecture for the three-dimensional reconstruction of outdoor environments in challenging terrain conditions is presented. The architecture proposed is configured as an Autoencoder. However, instead of the typical convolutional layers, some differences are proposed. The Encoder stage is set as a residual net with four residual blocks, which have been provided with the necessary knowledge to extract the feature maps from aerial images of outdoor environments. On the other hand, the Decoder stage is set as a Generative Adversarial Network (GAN) and called a GAN-Decoder. The proposed network architecture uses a sequence of the 2D aerial image as input. The Encoder stage works for the extraction of the vector of features that describe the input image, while the GAN-Decoder generates a point cloud based on the information obtained in the previous stage. By supplying a sequence of frames that a percentage of overlap between them, it is possible to determine the spatial location of each generated point. The experiments show that with this proposal it is possible to perform a 3D representation of an area flown over by a drone using the point cloud generated with a deep architecture that has a sequence of aerial 2D images as input. In comparison with other works, our proposed system is capable of performing three-dimensional reconstructions in challenging urban landscapes. Compared with the results obtained using commercial software, our proposal was able to generate reconstructions in less processing time, with less overlapping percentage between 2D images and is invariant to the type of flight path.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112311551