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Advances in Computer Graphics and 3D Technologies
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Advances in Computer Graphics and 3D Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2975

Special Issue Editors

Prof. Dr. Lihua You

E-Mail Website
Guest Editor
National Centre for Computer Animation, Bournemouth University, Bournemouth BH12 5BB, UK
Interests: geometric modeling; computer animation; computer graphics; image and point cloud-based shape reconstruction; machine learning; applications of ODEs and PDEs in geometric modeling and computer animation
Special Issues, Collections and Topics in MDPI journals
Dr. Zhidong Xiao

E-Mail Website
Guest Editor
National Centre for Computer Animation, Bournemouth University, Poole BH12 5BB, UK
Interests: computer graphics; motion capture; machine learning; motion synthesis; physics-based simulation; 3D reconstruction; virtual reality and robotics

Special Issue Information

Dear Colleagues,

Computer graphics and 3D technology have made a significant impact on our daily life, ranging from fields such as the entertainment industry (film, gaming, AR/VR) and healthcare medical visualisation to industrial digital twins. With the development of AI and machine learning, the production process will be shortened, and more realistic 3D computer graphics will be available for applications. Therefore, this Special Issue will present new ideas and experimental results in the fields of 3D computer graphics, real-time graphics, computer vision and machine learning, ranging from the design, algorithm development and theoretical stages to the graphics’ practical use.

Areas relevant to computer graphics, geometric modelling, computational geometry, computational photography, 3D reconstruction, shape and surface modelling include, but are not limited, to real-time graphics rendering techniques, volume rendering, computer animation and simulation, physically based modelling, computer vision for computer graphics, machine learning for graphics, data compression for graphics, metaverse (VR/MR/XR), computational fabrication and scientific visualisation.

Prof. Dr. Lihua You
Dr. Zhidong Xiao
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. Applied Sciences 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

  • geometric computing
  • computer graphics
  • computational geometry
  • physically based modelling
  • computational photography
  • 3D reconstruction
  • shape matching
  • shape and surface modelling

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Published Papers (2 papers)

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Research

16 pages, 3077 KiB  
Article
SS3DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
by Muhammad Awais Shoukat, Allah Bux Sargano, Alexander Malyshev, Lihua You and Zulfiqar Habib
Appl. Sci. 2024, 14(23), 11424; https://doi.org/10.3390/app142311424 - 8 Dec 2024
Viewed by 1066
Abstract
Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based [...] Read more.
Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based approaches can be divided into two-stage and single-stage methods, each with limitations. Two-stage methods often rely on generating intermediate proposals by searching for similar structures across the entire dataset, a process that is computationally expensive due to the large search space and high-dimensional feature-matching requirements, further limiting flexibility to predefined object categories. In contrast, single-stage methods directly reconstruct 3D shapes from images without intermediate steps, but they struggle to capture complex object geometries due to high feature loss between image features and 3D shapes and limit their ability to represent intricate details. To address these challenges, this paper introduces SS3DNet-AF, a single-stage, single-view 3D reconstruction network with an attention-based fusion (AF) mechanism to enhance focus on relevant image features, effectively capturing geometric details and generalizing across diverse object categories. The proposed method is quantitatively evaluated using the ShapeNet dataset, demonstrating its effectiveness in achieving accurate 3D reconstructions while overcoming the computational challenges associated with traditional approaches. Full article
(This article belongs to the Special Issue Advances in Computer Graphics and 3D Technologies)
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24 pages, 8793 KiB  
Article
A Novel Computational Paradigm for Reconstructing Solid CAD Features from a Segmented Manifold Triangular Mesh
by Feiyu Zhao
Appl. Sci. 2024, 14(14), 6183; https://doi.org/10.3390/app14146183 - 16 Jul 2024
Viewed by 1148
Abstract
We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. [...] Read more.
We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. We categorize four prevalent features, namely extrusion, rotation, sweep, and loft, as generalized swept bodies driven by cross-sectional sketches and feature paths, providing a unified mathematical representation for various feature types. The numerical optimization-based approach conducts geometric processing on the segmented manifold triangular mesh patch, extracting cross-sectional sketch curves and feature paths from its surface, and then reconstructing appropriate features using the Open CASCADE kernel. We employ the personalized three-dimensional (3D) printed model as a case study. Parametric and variant designs of the 3D-printed models are achieved through feature reconstruction of the manifold triangular mesh obtained via 3D scanning. Full article
(This article belongs to the Special Issue Advances in Computer Graphics and 3D Technologies)
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