Mathematical Applications in Computer Graphics

Dear Colleagues,

Mathematics serves as the foundation for many of the most significant advancements in computer graphics. From geometric modeling to rendering techniques, mathematical principles enable the development of sophisticated algorithms that drive the creation and manipulation of complex visual data. This Special Issue aims to bring together cutting-edge research that leverages mathematical methodologies to address challenges and innovate within the field of computer graphics.

The convergence of mathematics and computer graphics has led to transformative progress in various applications, including virtual and augmented reality, digital arts, animation, and scientific visualization. However, many challenges remain, particularly in the efficient processing and rendering of complex data structures, real-time interaction, and accurate simulation of physical phenomena. This Special Issue seeks to highlight novel mathematical approaches that push the boundaries of what is possible in computer graphics.

We invite prospective authors to submit original manuscripts that explore mathematical techniques and their applications in computer graphics. Topics of interest include, but are not limited to, the following:

• Geometric and solid modeling;
• Techniques for geometric and solid modeling;
• Curve and surface reconstruction;
• Shape and surface modeling;
• Rendering techniques;
• Physically based rendering;
• Global illumination;
• Image-based rendering;
• Simulation and animation;
• Physically based animation;
• Simulation of natural phenomena;
• Computational photography and video processing;
• Virtual and augmented reality;
• VR/AR rendering techniques;
• Interaction models in virtual environments;
• Real-time graphics for VR/AR applications;
• Data visualization and analysis;
• Scientific visualization;
• Visual analytics;
• Information visualization;
• Mathematical foundations;
• Computational geometry;
• Data compression for graphics;
• Numerical methods in graphics;
• Machine learning and AI in graphics;
• Geometry-based machine learning models;
• Deep learning for surface reconstruction and optimization;
• Graph convolutional networks for graphics;
• Applications in various domains;
• Medical imaging and visualization;
• Digital cultural heritage;
• Computational fabrication.

Dr. **aoqiang Zhu
Prof. Dr. Lihua You
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Mathematics 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 2600 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.

• geometric and solid modeling
• visual analytics
• computational geometry
• numerical methods in graphics
• machine learning and AI in graphics
• geometry-based machine learning models
• deep learning for surface reconstruction and optimization
• graph convolutional networks for graphics