Advances in Computer Graphics and Visual Computing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 307

Special Issue Editor


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Guest Editor
Computer Science Department, The University of Auckland, Auckland 92019, New Zealand
Interests: computer graphics; scientific visualisation; biomedical visualisation; game technology; virtual reality (VR); augmented reality (AR); human–computer interfaces (HCIs)
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Special Issue Information

Dear Colleagues,

This Special Issue explores advances in computer graphics and visual computing which have helped to reshape the future of visual technologies and have been used for innovative applications. This Special Issue highlights developments along the intersection of computer graphics, computer vision, and human–computer interaction. This Special Issue serves as a platform for showcasing both theoretical advancements and practical applications, bridging the gap between academia and industry.

Key topics include the following: (1) novel computer graphics (CG) techniques such as procedural content generation, neural rendering, novel animation techniques, immersive virtual and augmented reality experiences, advancements in GPU programming and parallel computing, and game technologies and serious games; (2) novel computer vision (CV) techniques such as 3D reconstruction, object tracking, and object recognition; and (3) HCI research related to CG and CV such as novel VR interfaces, the detection of user parameters such as emotion and cognitive load, and the customization and personalization of interfaces. Special attention is given to how visual computing is enhancing accessibility, user experience, and communication across different media.

This Special Issue also explores the integration of artificial intelligence (AI) in visual computing, focusing on how AI is revolutionizing animation, image synthesis, predictive simulations, 3D content generation, and scene interpretation. Contributions from renowned experts and emerging scholars will provide a comprehensive overview of current trends, challenges, and opportunities in the field.

By examining these themes, this Special Issue offers valuable insights into the transformative potential of visual computing, influencing industries from entertainment and gaming to healthcare, education, security, logistics, and manufacturing.

Dr. Burkhard Wünsche
Guest Editor

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. Information is an international peer-reviewed open access monthly 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 1800 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

  • neural rendering and GenAI for image synthesis
  • photorealistic and non-photorealistic rendering
  • 3D modelling and 3D content creation
  • medical, scientific, and information visualization
  • medical imaging and virtual surgery
  • virtual and augmented reality
  • game technologies and serious games
  • object detection and object tracking
  • 3D vision and reconstruction
  • multimodal learning for computer vision
  • explainable AI in computer vision
  • domain adaption and generalization of vision models
  • ethics and bias in visual computing
  • novel AR/VR interfaces and navigation
  • customization and personalization in visual computing
  • brain–computer interfaces for visual computing
  • human–robot interaction and teleoperation
  • accessibility and equality in visual computing

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Published Papers (1 paper)

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Research

16 pages, 8334 KiB  
Article
A Graph Laplacian Regularizer from Deep Features for Depth Map Super-Resolution
by George Gartzonikas, Evaggelia Tsiligianni, Nikos Deligiannis and Lisimachos P. Kondi
Information 2025, 16(6), 501; https://doi.org/10.3390/info16060501 - 17 Jun 2025
Viewed by 199
Abstract
Current depth map sensing technologies capture depth maps at low spatial resolution, rendering serious problems in various applications. In this paper, we propose a single depth map super-resolution method that combines the advantages of model-based methods and deep learning approaches. Specifically, we formulate [...] Read more.
Current depth map sensing technologies capture depth maps at low spatial resolution, rendering serious problems in various applications. In this paper, we propose a single depth map super-resolution method that combines the advantages of model-based methods and deep learning approaches. Specifically, we formulate a linear inverse problem which we solve by introducing a graph Laplacian regularizer. The regularization approach promotes smoothness and preserves the structural details of the observed depth map. We construct the graph Laplacian matrix by deploying latent features obtained from a pretrained deep learning model. The problem is solved with the Alternating Direction Method of Multipliers (ADMM). Experimental results show that the proposed approach outperforms existing optimization-based and deep learning solutions. Full article
(This article belongs to the Special Issue Advances in Computer Graphics and Visual Computing)
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