Gap completion in point cloud scene occluded by vehicles using SGC-Net

verfasst von: Yu Feng, Yiming Xu, Yan Xia, Claus Brenner, Monika Sester
Abstract: Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower sections of buildings. In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion. We have developed an innovative technique where we place virtual vehicle models along road boundaries in the gap-free scene and utilize a ray-casting algorithm to create a new scene with occluded gaps. This allows us to generate diverse and realistic urban point cloud scenes with and without vehicle occlusion, surpassing the limitations of real-world training data collection and annotation. Furthermore, we introduce the Scene Gap Completion Network (SGC-Net), an end-to-end model that can generate well-defined shape boundaries and smooth surfaces within occluded gaps. The experiment results reveal that 97.66% of the filled points fall within a range of 5 centimeters relative to the high-density ground truth point cloud scene. These findings underscore the efficacy of our proposed model in gap completion and reconstructing urban scenes affected by vehicle occlusions.
Organisationseinheit(en): Institut für Kartographie und Geoinformatik
Leibniz Forschungszentrum FZ:GEO
Externe Organisation(en): Technische Universität München (TUM)
Typ: Artikel
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Band: 215
Seiten: 331-350
Anzahl der Seiten: 20
ISSN: 0924-2716
Publikationsdatum: 09.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Atom- und Molekularphysik sowie Optik, Ingenieurwesen (sonstige), Angewandte Informatik, Computer in den Geowissenschaften
Elektronische Version(en): https://doi.org/10.1016/j.isprsjprs.2024.07.009 (Zugang: Offen)

BibTeX

@article{53307baa4f6144c1b9fd05f5d7c77f0b,
title = "Gap completion in point cloud scene occluded by vehicles using SGC-Net",
abstract = "Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower sections of buildings. In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion. We have developed an innovative technique where we place virtual vehicle models along road boundaries in the gap-free scene and utilize a ray-casting algorithm to create a new scene with occluded gaps. This allows us to generate diverse and realistic urban point cloud scenes with and without vehicle occlusion, surpassing the limitations of real-world training data collection and annotation. Furthermore, we introduce the Scene Gap Completion Network (SGC-Net), an end-to-end model that can generate well-defined shape boundaries and smooth surfaces within occluded gaps. The experiment results reveal that 97.66% of the filled points fall within a range of 5 centimeters relative to the high-density ground truth point cloud scene. These findings underscore the efficacy of our proposed model in gap completion and reconstructing urban scenes affected by vehicle occlusions.",
keywords = "3D scene completion, LiDAR mobile mapping, Point cloud processing",
author = "Yu Feng and Yiming Xu and Yan Xia and Claus Brenner and Monika Sester",
note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",
year = "2024",
month = sep,
doi = "10.1016/j.isprsjprs.2024.07.009",
language = "English",
volume = "215",
pages = "331--350",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier",
}