3D building fabrication with geometry and texture coordination via hybrid GAN

Du, Zhenlong; Shen, Haiyang; Li, Xiaoli; Wang, Meng

doi:10.1007/s12652-020-02488-9

Cited by 8 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coverage [67], [70], [135] [68], [74], [114], [71], [79], [113], [72], [77], [136], [5] [56], [69] Surface distance [36], [83], [84] Light Field Distance [5], [85] EMD [132] [70], [137], [138], [34], [63], [76], [18] [68], [74], [79], [71], [139], [140], [72], [113] [56], [69] [75] MMD [37] [5], [67], [70] [68], [79], [140] [69]…”

Section: Objective Similaritymentioning

confidence: 99%

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Fukaya,

Daylamani-Zad,

Agius

2024

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

Generative systems for graphical assets have the potential to provide users with high quality assets at the push of a button. However, there are many forms of assets, and many approaches for producing them. Quantitative evaluation of these methods is necessary if practitioners wish to validate or compare their implementations. Furthermore, providing benchmarks for new methods to strive for or surpass. While most methods are validated using tried-and-tested metrics within their own domains, there is no unified method of finding the most appropriate. We present a framework based on a literature pool of close to 200 papers, that provides guidance in selecting metrics to evaluate the validity and quality of artefacts produced, and the operational capabilities of the method.

show abstract

Section: Objective Similaritymentioning

confidence: 99%

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Fukaya,

Daylamani-Zad,

Agius

2024

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…These learning-based models are capable of handling big datasets of urban scenes having lots of different objects, such as buildings, roads, vegetation, and parking area. Many deep learning based methods [52], [53], [66], [67], [68] have address the urban reconstruction from multiview images or satellite imagery, while point cloud-based urban reconstruction [54], [55] and lightweight reconstruction approaches are considered by fewer researchers. As follows, a number of deep learning urban reconstruction approaches with the aim of lightweight reconstruction are reviewed.…”

Section: Deep Learning Based Algorithmsmentioning

confidence: 99%

“…Furthermore, this representation lacks sufficient detailing to guarantee seamless textures at boundary areas. An improvement to FrankenGAN is proposed in [68], which has replaced BicycleGAN with Star-GAN architecture to generate higher quality textures.…”

Section: Deep Learning Based Algorithmsmentioning

confidence: 99%

Lightweight Reconstruction of Urban Buildings: Data Structures, Algorithms, and Future Directions

Kamra

Kudeshia

ArabiNaree

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Commercial buildings as well as residential houses represent core structures of any modern day urban or semiurban areas. Consequently, 3-D models of urban buildings are of paramount importance to a majority of digital urban applications, such as city planning, 3-D mapping and navigation, video games and movies, and construction progress tracking, among others. However, current studies suggest that existing 3-D modeling approaches often involve high computational cost and large storage volumes for processing the geometric details of the buildings. Therefore, it is essential to generate concise digital representations of urban buildings from the 3-D measurements or images so that the acquired information can be efficiently utilized for various urban applications. Such concise representations, often referred to as "lightweight" models, strive to capture the details of the physical objects with less computational storage. Furthermore, lightweight models consume less bandwidth for online applications and facilitate accelerated visualizations. With many emerging digital urban infrastructure applications, lightweight reconstruction is poised to become a new area of research in the urban remote sensing community. We aim to provide a thorough review of data structures, representations, and state-of-the-art algorithms for lightweight 3-D urban reconstruction. We discuss the strengths and weaknesses of key lightweight urban reconstruction techniques, ultimately providing guidance on future research prospects to fulfill the pressing needs of urban applications.

show abstract

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Xu,

Omitaomu,

Sabri

et al. 2024

Urban Info

View full text Add to dashboard Cite

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

show abstract

3D building fabrication with geometry and texture coordination via hybrid GAN

Cited by 8 publications

References 34 publications

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Evaluation Metrics for Intelligent Generation of Graphical Game Assets: A Systematic Survey-Based Framework

Lightweight Reconstruction of Urban Buildings: Data Structures, Algorithms, and Future Directions

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Contact Info

Product

Resources

About