Sequential construction of 3-D-based scene description

Tsai, Chun-Jen; Katsaggelos, A.K.

doi:10.1109/76.845002

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…A collaborative design platform that connects all of its different specialties can also use this to keep track of changes in construction volume caused by design changes and link them to its own estimates at any time. It can not only make the work more efficient but it can also make sure that there are not any mistakes in the design because of the coordination of different professions [31][32][33][34][35][36][37][38]. In the 3D model, more no geometric data like value boundaries and market data can be added.…”

Section: Simulation Outcomementioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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The developing intricacy and extent of building projects, as well as the way that construction schedule management is still mostly done by hand, has resulted in low efficiency in construction schedule management, resulting in cost overruns and legal disputes as a result of schedule delays in many projects. Existing 3D reconstruction algorithms frequently result in large there may be holes, distortions, or hazy regions in remade 3D models, but AI-based 3D remaking strategies regularly reestablish straightforward isolated parts and portray them as 3D boxes. Accordingly, these algorithmic systems are generally not sufficient for certifiable use. The primary objective of this paper is to apply the creation ill-disposed network strategy to 3D recreation works on the nature of the underlying 3D recreation model via preparing a generative ill-disposed network model to a joined state. As solo examples, just recently noticed 2D pictures are required, with no dependence on earlier information on the 3D hidden shape or reference insights. Test results show that this algorithmic structure outflanks present status of-the-workmanship 3D reproduction approaches on an average 3D recreation test set. On the common 3D remaking test set, exploratory outcomes uncover that this algorithmic structure beats the current cutting edge 3D reproduction techniques.

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Section: Simulation Outcomementioning

confidence: 99%

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Wang

2022

Wireless Communications and Mobile Computing

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“…For different reconstruction targets, researchers will try to present the reconstructed 3D models in different ways. Common forms of presentation include stereo body elements [6], point clouds [8], and a combination of mesh skeleton and surface textures [30]. In recent years, researchers have made great progress in the research of new methods for 3D reconstruction techniques.…”

Section: Related Workmentioning

confidence: 99%

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Zhou

2021

Scientific Programming

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The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.

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Dependable management system for ubiquitous camera array service in an elder-care center

Bharanitharan

Ding

Paul

et al. 2013

ACM Trans. Embed. Comput. Syst.

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The concept of smart homes (SH) has been extensively popularized, and there are a lot of technologies that need to be continuously utilized and integrated in such a concept. In this article, some applied problems of camera array (CA) in the SH are discussed and solved. Determining how to build an effective management method for CA in order to ensure that user privacy is not encroached upon is an important issue. In SH, the applications of CA are very diversified. We suggest that a satisfactory management method of CA should be based on the open service gateway initiative (OSGi) that includes resource management and monitoring (RMM) and UPnP security for the problems of resources and privacy, respectively. Finally, an applied example of CA is addressed in an elder-care center (EC). Simulation results show that the management strategy and application of CA based on an OSGi is satisfactory.

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Sequential construction of 3-D-based scene description

Cited by 3 publications

References 24 publications

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Construction of 3D Reconstruction System for Building Construction Scenes Based on Deep Learning and IoT

Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Dependable management system for ubiquitous camera array service in an elder-care center

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