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New Advances in Visual Computing and Virtual Reality, 2nd Edition
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New Advances in Visual Computing and Virtual Reality, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 25 November 2024 | Viewed by 1133

Special Issue Editors

Dr. Hai Huang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: computer vision; virtual reality; digital twin
Special Issues, Collections and Topics in MDPI journals
Dr. Na Jiang

E-Mail Website
Guest Editor
Information Engineering College, Capital Normal University, Beijing 100048, China
Interests: computer vision; virtual reality
Dr. Yuehua Wang

E-Mail Website
Guest Editor
Department of Computer Science and Information Systems, Texas A&M University, Commerce, TX 75428, USA
Interests: autonomous driving; computer vision; cyber physical systems; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Visual computing and virtual reality are key technologies that will facilitate a major paradigm shift in the way users interact with data, and have been recognized as a viable solution for solving many critical needs. Both visual computing and virtual reality handle images and 3D models, i.e., computer graphics, image processing, visualization, computer vision, virtual and augmented reality, and video processing, but also include aspects of pattern recognition, human–computer interaction, and machine learning. In particular, machine learning ushers in a new wave of innovation in computer vision and computer graphics, which is gradually bringing visual computing and virtual reality to a whole new level.

The aim of this Special Issue of Electronics is to seek high-quality submissions that highlight emerging applications and address recent breakthroughs in the broad area of visual computing and virtual reality, including virtual reality (VR), augmented reality (AR), mixed reality (MR), 3D interaction, visualization, computer graphics, computer vision, and deep learning. We invite researchers to contribute original and unique articles, as well as sophisticated review articles. Topics include, but are not limited to, the following areas:

  • Visualization;
  • VR/AR/MR computer graphics;
  • 3D object reconstruction;
  • 3D deep learning;
  • Signal and image processing;
  • Deep learning for computer vision;
  • Image and video communication;
  • Tracking and sensing;
  • Human–computer interaction;
  • 3D display techniques and display devices;
  • Modeling, simulation, and animation;
  • Emerging applications and systems, including techniques, performance, and implementation.

Dr. Hai Huang
Dr. Na Jiang
Dr. Yuehua Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • visualization
  • computer graphics
  • 3D object reconstruction
  • deep learning
  • Signal and image processing
  • computer vision
  • image and video communication
  • tracking and sensing
  • human–computer interaction
  • 3D display techniques and display devices
  • modeling, simulation, and animation

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Published Papers (1 paper)

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Research

21 pages, 4314 KiB  
Article
RSCAN: Residual Spatial Cross-Attention Network for High-Fidelity Architectural Image Editing by Fusing Multi-Latent Spaces
by Cheng Zhu, Guangzhe Zhao, Benwang Lin, Xueping Wang and Feihu Yan
Electronics 2024, 13(12), 2327; https://doi.org/10.3390/electronics13122327 - 14 Jun 2024
Viewed by 682
Abstract
Image editing technology has brought about revolutionary changes in the field of architectural design, garnering significant attention in both the computer and architectural industries. However, architectural image editing is a challenging task due to the complex hierarchical structure of architectural images, which complicates [...] Read more.
Image editing technology has brought about revolutionary changes in the field of architectural design, garnering significant attention in both the computer and architectural industries. However, architectural image editing is a challenging task due to the complex hierarchical structure of architectural images, which complicates the learning process for the high-dimensional features of architectural images. Some methods invert the images into the latent space of a pre-trained generative adversarial network (GAN) model, completing the editing process by manipulating this latent space. However, the task of striking a balance between reconstruction fidelity and editing efficacy through latent space mapping presents a formidable challenge. To address this issue, we propose a Residual Spatial Cross-Attention Network (RSCAN) for architectural image editing, which is an encoder model integrating multiple latent spaces. Specifically, we introduce the spatial feature extractor, which maps the image to the high-dimensional space F of the synthesis network, to enhance the spatial information retention and preserve the structural consistency of the architectural image. In addition, we propose the residual cross-attention to learn the mapping relationship between the low-dimensional space W and F space, generating modified features corresponding to the latent code and leveraging the benefits of multiple latent spaces to facilitate editing. Extensive experiments are performed on the LSUN Church dataset, and the experimental results indicate that our proposed RSCAN achieves significant improvements over the relevant methods in quantitative analysis metrics including the reconstruction quality, SSIM, FID, L2, LPIPS, PSNR, and editing effect ΔS, with enhancements of 29.49%, 17.29%, 8.81%, 11.43%, 11.26%, and 47.8%, respectively, thereby enhancing the practicality of architectural image editing. Full article
(This article belongs to the Special Issue New Advances in Visual Computing and Virtual Reality, 2nd Edition)
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