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

This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cone...

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Veröffentlicht in:arXiv.org 2022-07
Hauptverfasser: Romanengo, Chiara, Raffo, Andrea, Biasotti, Silvia, Falcidieno, Bianca, Vlassis Fotis, Romanelis, Ioannis, Psatha, Eleftheria, Moustakas, Konstantinos, Sipiran, Ivan, Nguyen, Quang-Thuc, Chi-Bien Chu, Nguyen-Ngoc, Khoi-Nguyen, Dinh-Khoi Vo, Tuan-An To, Nham-Tan Nguyen, Le-Pham, Nhat-Quynh, Hai-Dang Nguyen, Tran, Minh-Triet, Qie, Yifan, Anwer, Nabil
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creator Romanengo, Chiara
Raffo, Andrea
Biasotti, Silvia
Falcidieno, Bianca
Vlassis Fotis
Romanelis, Ioannis
Psatha, Eleftheria
Moustakas, Konstantinos
Sipiran, Ivan
Nguyen, Quang-Thuc
Chi-Bien Chu
Nguyen-Ngoc, Khoi-Nguyen
Dinh-Khoi Vo
Tuan-An To
Nham-Tan Nguyen
Le-Pham, Nhat-Quynh
Hai-Dang Nguyen
Tran, Minh-Triet
Qie, Yifan
Anwer, Nabil
description This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cones and tori. The aim of the track is to evaluate the quality of automatic algorithms for fitting and recognising geometric primitives on point clouds. Specifically, the goal is to identify, for each point cloud, its primitive type and some geometric descriptors. For this purpose, we created a synthetic dataset, divided into a training set and a test set, containing segments perturbed with different kinds of point cloud artifacts. Among the six participants to this track, two are based on direct methods, while four are either fully based on deep learning or combine direct and neural approaches. The performance of the methods is evaluated using various classification and approximation measures.
doi_str_mv 10.48550/arxiv.2206.07636
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subjects Algorithms
Computer Science - Graphics
Computer Science - Numerical Analysis
Constructive solid geometry
Machine learning
Mathematics - Numerical Analysis
Recognition
Toruses
title SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
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