3D Point Cloud Object Completion via RGB Images with Complex Geometric Topology in Urban Scenes

The increasing deployment of UAVs as mobile communication relays in urban environments necessitates accurate 3D modeling of complex urban areas for optimal communication. Current practices involve LiDAR-based 3D scanning to generate point cloud data; however, sensor limitations and adverse weather c...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2024-10, p.1-1
Main Authors: Liu, Na, Zhang, Xu, Wang, Li, Xu, Lianming, Zhu, Sha
Format: Article
Language:English
Subjects:
Autonomous aerial vehicles
Buildings
Cameras
Data mining
Decoding
Feature extraction
generating adversarial networks
Generative adversarial networks
Generators
Image color analysis
MLP mechanism optimization
Multi-modal fusion
point cloud completion
Point cloud compression
urban scene modeling
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Summary:The increasing deployment of UAVs as mobile communication relays in urban environments necessitates accurate 3D modeling of complex urban areas for optimal communication. Current practices involve LiDAR-based 3D scanning to generate point cloud data; however, sensor limitations and adverse weather conditions may compromise data quality. This study proposes a new multi-modal point cloud and image fusion completion network (PIFC-Net) based on a generative adversarial network (GAN), specifically tailored for large-scale urban environments. The experimental study tested five different building shapes and various objects, and the results commend the network for its effectiveness in enhancing the quality and efficiency of point cloud completion.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3488724