Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

Honkavaara, Eija; Καράντζαλος, Κωνσταντίνος; Liang, Xinlian; Nocerino, Erica; Pölönen, Ilkka; Rönnholm, Petri

doi:10.3390/rs11141714

Cited by 1 publication

(1 citation statement)

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“…The nasolabial angle and facial convex angle in the study group are significantly larger than those in the control group, and the difference is statistically significant [24]. Papers on multimodal data using spectral imaging and 3D technology in remote sensing, including new sensors, developing machine learning technology for data analysis, and the application of these technologies in various geospatial applications [25].…”

Section: Introductionmentioning

confidence: 94%

[Retracted] Display and Analysis of Virtual 3D Technology in Interior Design

Sen

Yang²

2023

Journal of Sensors

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People are not satisfied with the two-dimensional technology, and three-dimensional virtual technology gradually enters every aspect of people’s daily life: medical treatment, education, social interaction, vision, and so on. Virtual 3D technology brings a lot of convenience to people’s lives and plays a core role in indoor scene design and layout. It enables users to see their own virtual indoor furniture and vegetation layout in advance and select and modify their own needs. We put forward several characteristics of indoor furniture selection and placement, such as no space restriction, interactivity and fault tolerance, and advanced display. We construct the basic algorithm of image transformation registration detection and finally optimize the basic algorithm with three different deep learning algorithm models, and get that the convolution layer neural network algorithm is superior to the other two models not only in the selection and placement of virtual furniture but also in the layout design of virtual vegetation landscape. Finally, for image defect detection, we compare the time cost of three models, which further shows that the convolution layer combined with image transformation technology model is fast and efficient.

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Section: Introductionmentioning

confidence: 94%