SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds

Romanengo, Chiara; Raffo, Andrea; Biasotti, Silvia; Falcidieno, Bianca; Fotis, Vlassis; Romanelis, Ioannis; Eleftheria, Psatha,; Μουστάκας, Κωνσταντίνος; Sipiran, Ivan; Nguyen, Quan Dinh; Chu, Chi-Bien; Nguyen-Ngoc, Khoi-Nguyen; Vo, Dinh-Khoi; To, Tuan-An; Nguyen, Nham-Tan; Le-Pham, Nhat-Quynh; Nguyen, Hai-Dang; Tran, Minh-Triet; Qie, Yifan; Anwer, Nabil

doi:10.1016/j.cag.2022.07.004

Cited by 8 publications

(1 citation statement)

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“…The work in [9] also addresses the problem of reducing the number of the parameters of the primitive representation when the point cloud corresponds to a patch of a primitive: in this case, the point cloud is opportunely rototranslated in a space embedding so that it can be fit by a primitive in a standard form. The method has been recently compared with other direct and learning-based approaches, see [32]. However, the point clouds used for this comparison did neither originate from scans of mechanical CAD objects nor can be assumed to represent presegmented data, making the benchmark itself not of interest to this paper.…”

Section: Previous Workmentioning

confidence: 99%

Recognizing geometric primitives in 3D point clouds of mechanical CAD objects

Romanengo

Raffo

Biasotti

et al. 2023

Computer-Aided Design

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Section: Previous Workmentioning

confidence: 99%

Recognizing geometric primitives in 3D point clouds of mechanical CAD objects

Romanengo

Raffo

Biasotti

et al. 2023

Computer-Aided Design

Self Cite

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CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

Raffo,

Ranieri,

Romanengo

et al. 2024

Vis Comput

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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.

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