Automatic 3d Buildings Compact Reconstruction From Lidar Point Clouds

Nys, Gilles‐Antoine; Billen, Roland; Poux, Florent

doi:10.5194/isprs-archives-xliii-b2-2020-473-2020

Cited by 9 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The software has support for various formats. In addition, the authors in (Nys, Billen, et Poux 2020) have proposed an automatic CityJSON workflow that extracts roof surfaces from LiDAR data and generates LoD2.1 building models. Another related work, City3D, was conducted by (Huang et al 2022), presenting a large-scale 3D building reconstruction from the ALS point cloud.…”

Section: Related Workmentioning

confidence: 99%

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

Ballouch,

Jeddoub,

Hajji

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The emergence of Digital Twins in city planning and management marks a contemporary trend, elevating the realm of 3D modeling and simulation for cities. In this context, the use of semantic point clouds to generate 3D city models for Digital Twins proves instrumental in addressing this evolving need. This article introduces a processing pipeline for the automatic modeling of buildings, roads, and vegetation based on the semantic segmentation results of 3D LiDAR point clouds. It employs a semantic segmentation approach that integrates multiple training datasets to achieve precise extraction of target objects. Open-source reconstruction tools have been adapted for building and road modeling, while a Python code was optimized for tree modeling, leveraging a foundational code. The case study was conducted in the city of Liège, Belgium. The obtained results were satisfactory, and the schemas and geometry of the developed models were validated. An evaluation of the adopted reconstruction methods was conducted, along with their comparison to other methods from the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

Ballouch,

Jeddoub,

Hajji

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Its improved support of levels of detail and metadata make it a good substitute for CityGML (Nys, Poux, & Billen, 2020). However, its usage is still limited to specific applications and data encoding (Kumar, Ledoux, & Stoter, 2018; Nys, Billen, & Poux, 2020; Virtanen et al., 2021). Besides, the new support of 3D models in QGIS should improve its usability thanks to the development of a CityJSON plugin (Vitalis, Arroyo Ohori, & Stoter, 2020).…”

Section: Related Workmentioning

confidence: 99%

From consistency to flexibility: A simplified database schema for the management of CityJSON 3D city models

Nys

Billen

2021

Transactions in GIS

View full text Add to dashboard Cite

The use of 3D city models is now common practice; many large cities have their own digital model. Resilient and sustainable management of these models is necessary in many cases, where an application could evolve over its life cycle. The complexity of generic modelling standardization is often a limitation for a light and user-friendly usage and further developments. This paper aims to propose an alternative providing a simplified database schema implemented in a document-oriented storage. Thanks to the use of the NoSQL store, the focus is on flexibility of the data schemas and thus its clarification. In order to aim attention at the compactness in web development, CityJSON has been chosen for the encoding of the 3D city models. Finally, a full-stack application (persistent storage, consistent edition and visualization of 3D city models) has been developed to handle the simplified schema and illustrates its capabilities in two practical use cases.

show abstract

Drone-Based AI and 3D Reconstruction for Digital Twin Augmentation

Liu²,

Hairul³

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.

show abstract

Automatic 3d Buildings Compact Reconstruction From Lidar Point Clouds

Cited by 9 publications

References 15 publications

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds

From consistency to flexibility: A simplified database schema for the management of CityJSON 3D city models

Drone-Based AI and 3D Reconstruction for Digital Twin Augmentation

Contact Info

Product

Resources

About