Andrea Raffo

• Raffo, A., Romanengo, C., Biasotti, S., Falcidieno, B., 2023. Recognising geometric primitives in 3D point clouds of mechanical CAD objects. Computer-Aided Design 157, 103479. https://doi.org/10.1016/j.cad.2023.103479
• Raffo, A., Romanengo, C., Falcidieno, B., Biasotti, S., 2022. Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure. Computer Aided Geometric Design 97, 102123. https://doi.org/10.1016/j.cagd.2022.102123
• Raffo, A., Gagliardi, L., Fugacci, U., Sagresti, L., Grandinetti, S., Brancato, G., Biasotti, S., Rocchia, W., 2022. Chanalyzer: A Computational Geometry Approach for the Analysis of Protein Channel Shape and Dynamics. Frontiers in Molecular Biosciences 9, 933924. https://doi.org/10.3389/fmolb.2022.933924

• Romanengo, C., Raffo, A., Biasotti, S., Falcidieno, B., Fotis, V., Romanelis, I., Psatha, E., Moustakas, K., Sipiran, I., Nguyen, Q.-T., Chu, C.-B., Nguyen-Ngoc, K.-N., Vo, D.-K., To, T.-A., Nguyen, N.-T., Le-Pham, N.-Q., Nguyen, H.-D., Tran, M.-T., Qie, Y., Anwer, N., 2022. SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds. Computers & Graphics 107, 32–49. https://doi.org/10.1016/j.cag.2022.07.004

• Gagliardi, L., Raffo, A., Fugacci, U., Biasotti, S., Rocchia, W., Huang, H., Amor, B.B., Fang, Y., Zhang, Y., Wang, X., Christoffer, C., Kihara, D., Axenopoulos, A., Mylonas, S., Daras, P., 2022. SHREC 2022: Protein–ligand binding site recognition. Computers & Graphics 107, 20–31. https://doi.org/10.1016/j.cag.2022.07.005

• Raffo, A., Fugacci, U., Biasotti, S., Rocchia, W., Liu, Y., Otu, E., Zwiggelaar, R., Hunter, D., Zacharaki, E.I., Psatha, E., Laskos, D., Arvanitis, G., Moustakas, K., Aderinwale, T., Christoffer, C., Shin, W.-H., Kihara, D., Giachetti, A., Nguyen, H.-N., Nguyen, T.-D., Nguyen-Truong, V.-T., Le-Thanh, D., Nguyen, H.-D., Tran, M.-T., 2021. SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties. Computers & Graphics 99, 1–21. https://doi.org/10.1016/j.cag.2021.06.010

• Raffo, Andrea; Ranieri, Andrea; Romanengo, Chiara; Falcidieno, Bianca & Biasotti, Silvia (2024). CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves. The Visual Computer. ISSN 0178-2789. doi: 10.1007/s00371-024-03292-8.
• Raffo, Andrea & Paulsen, Jonas (2023). The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. Briefings in Bioinformatics. ISSN 1467-5463. 24(5), p. 1–14. doi: 10.1093/bib/bbad302. Full text in Research Archive Show summary

  The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.

• Raffo, Andrea; Fugacci, Ulderico & Biasotti, Silvia (2023). GEO-Nav: A geometric dataset of voltage-gated sodium channels. Computers & graphics. ISSN 0097-8493. 115, p. 285–295. doi: 10.1016/j.cag.2023.06.023. Full text in Research Archive Show summary

  Voltage-gated sodium (Nav) channels constitute a prime target for drug design and discovery, given their implication in various diseases such as epilepsy, migraine and ataxia to name a few. In this regard, performing morphological analysis is a crucial step in comprehensively understanding their biological function and mechanism, as well as in uncovering subtle details of their mechanism that may be elusive to experimental observations. Despite their tremendous therapeutic potential, drug design resources are deficient, particularly in terms of accurate and comprehensive geometric information. This paper presents a geometric dataset of molecular surfaces that are representative of Nav channels in mammals. For each structure we provide three representations and a number of geometric measures, including length, volume and straightness of the recognized channels. To demonstrate the effective use of GEO-Nav, we have tested it on two methods belonging to two different categories of approaches: a sphere-based and a tessellation-based method.

• Romanengo, Chiara; Raffo, Andrea; Qie, Yifan; Anwer, Nabil & Falcidieno, Bianca (2021). Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects. Computers & graphics. ISSN 0097-8493. 102, p. 133–143. doi: 10.1016/j.cag.2021.09.013. Full text in Research Archive Show summary

  We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. This benchmark is meant to help both method developers and those who want to identify the best performing tools. The Fit4CAD dataset is composed by 225 high quality point clouds, each of which has been obtained by sampling a CAD object. The way these elements were created by using existing platforms and datasets makes the benchmark easily expandable. The dataset is already split into a training set and a test set. To assess performance and accuracy of the different primitive fitting methods, various measures are defined. To demonstrate the effective use of Fit4CAD, we have tested it on two methods belonging to two different categories of approaches to the primitive fitting problem: a clustering method based on a primitive growing framework and a parametric method based on the Hough transform.

• Raffo, Andrea & Biasotti, Silvia (2020). Weighted quasi-interpolant spline approximations: Properties and applications. Numerical Algorithms. ISSN 1017-1398. 87, p. 819–847. doi: 10.1007/s11075-020-00989-4. Full text in Research Archive Show summary

  Continuous representations are fundamental for modeling sampled data and performing computations and numerical simulations directly on the model or its elements. To effectively and efficiently address the approximation of point clouds, we propose the weighted quasi-interpolant spline approximation method (wQISA). We provide global and local bounds of the method and discuss how it still preserves the shape properties of the classical quasi-interpolation scheme. This approach is particularly useful when the data noise can be represented as a probabilistic distribution: from the point of view of non-parametric regression, the wQISA estimator is robust to random perturbations, such as noise and outliers. Finally, we show the effectiveness of the method with several numerical simulations on real data, including curve fitting on images, surface approximation, and simulation of rainfall precipitations.

• Raffo, Andrea & Biasotti, Silvia (2020). Data-driven quasi-interpolant spline surfaces for point cloud approximation. Computers & graphics. ISSN 0097-8493. 89, p. 144–155. doi: 10.1016/j.cag.2020.05.004. Full text in Research Archive Show summary

  In this paper we investigate a local surface approximation, the Weighted Quasi Interpolant Spline Approximation (wQISA), specifically designed for large and noisy point clouds. We briefly describe the properties of the wQISA representation and introduce a novel data-driven implementation, which combines prediction capability and complexity efficiency. We provide an extended comparative analysis with other continuous approximations on real data, including different types of surfaces and levels of noise, such as 3D models, terrain data and digital environmental data.

• Raffo, Andrea; Barrowclough, Oliver Joseph David & Muntingh, Agnar Georg Peder (2020). Reverse engineering of CAD models via clustering and approximate implicitization. Computer Aided Geometric Design. ISSN 0167-8396. 80. doi: 10.1016/j.cagd.2020.101876. Full text in Research Archive Show summary

  In applications like computer aided design, geometric models are often represented numerically as polynomial splines or NURBS, even when they originate from primitive geometry. For purposes such as redesign and isogeometric analysis, it is of interest to extract information about the underlying geometry through reverse engineering. In this work we develop a novel method to determine these primitive shapes by combining clustering analysis with approximate implicitization. The proposed method is automatic and can recover algebraic hypersurfaces of any degree in any dimension. In exact arithmetic, the algorithm returns exact results. All the required parameters, such as the implicit degree of the patches and the number of clusters of the model, are inferred using numerical approaches in order to obtain an algorithm that requires as little manual input as possible. The effectiveness, efficiency and robustness of the method are shown both in a theoretical analysis and in numerical examples implemented in Python.

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• Raffo, Andrea & Dokken, Tor (2019). Piecewise approximate implicitization with prescribed conditions using tensor-product B-splines.
• Raffo, Andrea; Barrowclough, Oliver Joseph David; Muntingh, Agnar Georg Peder & Skytt, Vibeke (2017). A machine learning approach to reverse engineering based on clustering and approximate implicitization.
• Raffo, Andrea; Barrowclough, Oliver Joseph David; Dahl, Heidi Elisabeth Iuell; Dokken, Tor; Floater, Michael S. & Muntingh, Agnar Georg Peder (2016). Locally refined approximate implicitisation for design and manufacturing.

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