@article{fotis2026prep,

title = {PReP: Efficient context-based shape retrieval for missing parts},

author = {Vlassis Fotis and Ioannis Romanelis and Georgios Mylonas and Athanasios Kalogeras and Konstantinos Moustakas},

url = {https://www.vvr.ece.upatras.gr/wp-content/uploads/sites/5/2025/01/PReP_-Efficient-context-based-shape-retrieval-for-missing-parts_compressed.pdf, pdf/preprint},

doi = {10.1109/TMM.2026.3668613},

year  = {2026},

date = {2026-02-27},

urldate = {2026-02-27},

journal = {IEEE Transactions on Multimedia},

volume = {28},

abstract = {In this paper, we study the problem of shape part retrieval in the point cloud domain. Existing shape retrieval methods typically rely on the presence of a complete query object, but what if the part of interest is missing? We present the Part Retrieval Pipeline (PReP), which combines metric learning techniques with a trained classification model to evaluate the suitability of potential replacement parts from a database, within an application scenario aimed at circular economy. Through a progressively more difficult training procedure, PReP learns to recognize suitable parts based solely on shape context. Owing to its compact parameterization and low computational requirements, it can search a repository of tens of thousands of spare parts in just a few seconds. We also establish an alternative baseline approach for comparison, document the unique challenges associated with this task, and identify key design choices to address them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{romanelis2025efficient,

title = {Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models},

author = {Ioannis Romanelis and Vlassios Fotis and Athanasios Kalogeras and Christos Alexakos and Konstantinos Moustakas and Adrian Munteanu},

url = {https://www.vvr.ece.upatras.gr/wp-content/uploads/sites/5/2025/01/Efficient-and-Scalable-Point-Cloud-Generation-with-Sparse-point-voxel-difussion-models_compressed.pdf, pdf/preprint},

doi = {10.1109/TNNLS.2025.3636409},

year  = {2025},

date = {2025-12-04},

urldate = {2025-12-04},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

volume = {37},

issue = {6},

abstract = {We propose a novel point cloud U-Net diffusion architecture for 3-D generative modeling capable of generating high-quality and diverse 3-D shapes while maintaining fast generation times. Our network employs a dual-branch architecture, combining the high-resolution representations of points with the computational efficiency of sparse voxels. Our fastest variant outperforms all nondiffusion generative approaches on unconditional shape generation, the most popular benchmark for evaluating point cloud generative models, while our largest model achieves state-of-the-art results among diffusion methods, with a runtime approximately 70% of the previously state-of-the-art point-voxel diffusion (PVD), measured on the same hardware setting. Beyond unconditional generation, we perform extensive evaluations, including conditional generation on all categories of ShapeNet, demonstrating the scalability of our model to larger datasets, and implicit generation, which allows our network to produce high-quality point clouds on fewer timesteps, further decreasing the generation time. Finally, we evaluate the architecture’s performance in point cloud completion and super-resolution. Our model excels in all tasks, establishing it as a state-of-the-art diffusion U-Net for point cloud generative modeling. The code is publicly available at https://github.com/JohnRomanelis/SPVD},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dvorak2025survey,

title = {Survey of Inter-Prediction Methods for Time-Varying Mesh Compression},

author = {Jan Dvořák and Filip Hácha and Gerasimos Arvanitis and David Podgorelec and Konstantinos Moustakas and Libor Váša},

url = {https://www.vvr.ece.upatras.gr/wp-content/uploads/sites/5/2025/01/Survey-of-Inter‐Prediction-Methods-for-Time‐Varying-Mesh-Compression_compressed.pdf, pdf/preprint},

doi = {https://doi.org/10.1111/cgf.15278},

year  = {2025},

date = {2025-01-13},

urldate = {2025-01-13},

journal = {Computer Graphics Forum},

pages = {e15278},

abstract = {Abstract Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}