Unlike image or text domains that benefit from an abundance of large-scale datasets, point cloud learning techniques frequently encounter limitations due to the scarcity of extensive datasets. To overcome this limitation, we present Symmetria, a formula-driven dataset that can be generated at any arbitrary scale. By construction, it ensures the absolute availability of precise ground truth, promotes data-efficient experimentation by requiring fewer samples, enables broad generalization across diverse geometric settings, and offers easy extensibility to new tasks and modalities. Using the concept of symmetry, we create shapes with known structure and high variability, enabling neural networks to learn point cloud features effectively. Our results demonstrate that this dataset is highly effective for point cloud self-supervised pre-training, yielding models with strong performance in downstream tasks such as classification and segmentation, which also show good few-shot learning capabilities. Additionally, our dataset can support fine-tuning models to classify real-world objects, highlighting our approach’s practical utility and application. We also introduce a challenging task for symmetry detection and provide a benchmark for baseline comparisons. A significant advantage of our approach is the public availability of the dataset, the accompanying code, and the ability to generate very large collections, promoting further research and innovation in point cloud learning.

This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large dataset of 11,555 protein surfaces with calculated electrostatic potential (a key molecular surface descriptor). The performance in retrieval of the proposed methods was evaluated through different metrics (Accuracy, Balanced accuracy, F1 score, Precision and Recall). The best retrieval performance was achieved by the proposed methods that used the electrostatic potential complementary to molecular surface shape. This observation was also valid for classes with limited data which highlights the importance of taking into account additional molecular surface descriptors.

Abstract We propose CurveML, a benchmark for evaluating and comparing methods for the classification and identification of plane curves represented as point sets. The dataset is composed of 520k curves, of which 280k are generated from specific families characterised by distinctive shapes, and 240k are obtained from Bézier or composite Bézier curves. The dataset was generated starting from the parametric equations of the selected curves making it easily extensible. It is split into training, validation, and test sets to make it usable by learning-based methods, and it contains curves perturbed with different kinds of point set artefacts. To evaluate the detection of curves in point sets, our benchmark includes various metrics with particular care on what concerns the classification and approximation accuracy. Finally, we provide a comprehensive set of accompanying demonstrations, showcasing curve classification, and parameter regression tasks using both ResNet-based and PointNet-based networks. These demonstrations encompass 14 experiments, with each network type comprising 7 runs: 1 for classification and 6 for regression of the 6 defining parameters of plane curves. The corresponding Jupyter notebooks with training procedures, evaluations, and pre-trained models are also included for a thorough understanding of the methodologies employed.

In this work, our main objective is to explore how Software Routers (SRs) can deploy advanced and flexible paradigms for supporting novel control plane functionalities and applications. To this purpose, we investigate and study a new open-source SW framework, namely Distributed SW ROuter Project (DROP), which aims at developing and enabling a novel distributed paradigm for IP-router control and management. DROP is partially based on the main guidelines of the IETF ForCES standard, and it allows building logical network nodes through the aggregation of multiple SRs, which can be devoted to packet forwarding or control operations. In addition to the original ForCES design, DROP will aim at extending router distribution and aggregation concepts by moving them at a network-wide scale in order to enable and to support value-added services for next-generation networks.

We consider a new generation of COTS software routers (SRs), able to effectively exploit multi-Core/CPU HW platforms. Our main objective is to evaluate and to model the impact of power saving mechanisms, generally included in today's COTS processors, on the SR networking performance and behavior. To this purpose, we separately characterized the roles of both HW and SW layers through a large set of internal and external experimental measurements, obtained with a heterogeneous set of HW platforms and SR setups. Starting from this detailed measure analysis, we propose a simple model, able to represent the SR performance with a high accuracy level in terms of packet throughput and related power consumption. The proposed model can be effectively applied inside "green optimization" mechanisms in order to minimize power consumption, while maintaining a certain SR performance target.