<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body style="overflow-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div>π’ Call for Papers β Special Issue on 3D Mesh Processing and Generation in the Age of Deep Learning</div><div><br></div><div>I am pleased to share the call for papers for the Special Issue:</div><div><br></div><div>β3D Mesh Processing and Generation in the Age of Deep Learningβ. This special issue is published in Image and Vision Computing and focuses on the latest advances at the intersection of 3D mesh processing, geometric deep learning, and generative AI.</div><div><br></div><div>Organized with Yu-Kun Lai, Lei Li, and Yiyi Liao, this special issue aims to bring together researchers working on learning-based approaches for understanding, processing, and generating 3D mesh data.</div><div><br></div><div>Three-dimensional (3D) meshes continue to serve as a core representation in visual computing, supporting a wide range of applications in scientific visualization, computer graphics, computer vision, virtual and augmented reality, and digital twin technologies. The rapid progress of deep learning has transformed how 3D data is processed, analyzed, and generated. Modern neural networks can now capture complex geometric priors, generate high-fidelity shapes, repair and simplify meshes, and enable intelligent 3D content creation with unprecedented realism and scalability.</div><div><br></div><div>This Special Issue seeks original contributions addressing both theoretical and practical challenges in areas such as:</div><div><br></div><div>β
Neural representations and implicit functions for 3D geometry</div><div>β
Deep 3D mesh generation, completion, and reconstruction</div><div>β
Learning-based mesh simplification, denoising, and remeshing</div><div>β
Mesh segmentation, classification, and correspondence learning</div><div>β
Neural mesh editing, deformation, and animation</div><div>β
Large-scale datasets and benchmarks for 3D mesh deep learning</div><div>β
Generative models, including GANs and diffusion models, for 3D mesh synthesis</div><div>β
Differentiable rendering and inverse graphics</div><div>β
Topology-aware neural network architectures</div><div>β
Multi-modal 3D learning from text, images, and video</div><div>β
Mesh quality metrics, fairness, and bias in 3D data generation</div><div>β
Applications in visualization, simulation, AR/VR, and industrial design</div><div><br></div><div>We warmly invite researchers from computer vision, computer graphics, geometric deep learning, and related fields to submit their latest contributions and help advance the next generation of intelligent 3D mesh processing.</div><div><br></div><div>π
Submission window: July 31, 2026 β March 15, 2027</div><div>π Article type: VSI: 3D Mesh Processing & DL</div><div>π More information and submission details:</div><div>https://www.sciencedirect.com/special-issue/335679/3d-mesh-processing-and-generation-in-the-age-of-deep-learning</div><div><br></div><div>Please feel free to share this call with colleagues and researchers who may be interested.</div><div><br></div><div>#CallForPapers #SpecialIssue #3DMesh #DeepLearning #GeometricDeepLearning #ComputerVision #ComputerGraphics #GenerativeAI #DiffusionModels #3DGeneration #MeshProcessing #ShapeAnalysis #DigitalTwins #ARVR #ImageAndVisionComputing</div><div>
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<span><b><span style="color:#000000;font-size:15px;font-family:helvetica, arial">Olivier LΓZORAY</span></b><br> Full Professor of Computer Science</span> <br><br>
<span style="color:#000000;font-size:15px;font-family:helvetica, arial"><b>University of Caen Normandy</b>
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<td>West Normandy Institute of Technology<br>Multimedia and Internet Department<br>F-50000 SAINT-LΓ
<a href="tel:+33 2 33 77 55 14" style="color:#3388cc;text-decoration:none">+33(0)233775514</a>
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GREYC UMR CNRS 6072<br>Image Team - ENSICAEN<br>6 Bd. Marechal Juin<br>F-14000 CAEN
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