3 papers
cs.CV2026
A 3D mesh convolution-based autoencoder for geometry compression
Germain Bregeon, Marius Preda, Radu Ispas +1
In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing no…
cs.CV2025
MeshConv3D: Efficient convolution and pooling operators for triangular 3D meshes
Germain Bregeon, Marius Preda, Radu Ispas +1
Convolutional neural networks (CNNs) have been pivotal in various 2D image analysis tasks, including computer vision, image indexing and retrieval or semantic classification. Exten…
eess.IV2025
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans
Rezkellah Noureddine Khiati, Pierre-Yves Brillet, Radu Ispas +1
Accurate quantification of the extent of lung pathological patterns (fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for diagnosis and follow-up of inters…