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

التفاصيل البيبلوغرافية
العنوان:	SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
المؤلفون:	Romanengo, Chiara, Raffo, Andrea, Biasotti, Silvia, Falcidieno, Bianca, Fotis, Vlassis, Romanelis, Ioannis, Psatha, Eleftheria, Moustakas, Konstantinos, Sipiran, Ivan, Nguyen, Quang-Thuc, 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
المصدر:	Computers & Graphics 107 (2022) 32-49
سنة النشر:	2022
المجموعة:	Computer Science Mathematics
مصطلحات موضوعية:	Computer Science - Graphics, Mathematics - Numerical Analysis, 68U05, 68U07, 65D18, 65D17, G.1.2, I.3.5, I.5.4
الوصف:	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.
نوع الوثيقة:	Working Paper
DOI:	10.1016/j.cag.2022.07.004
URL الوصول:	http://arxiv.org/abs/2206.07636
رقم الأكسشن:	edsarx.2206.07636
قاعدة البيانات:	arXiv

الوصف
DOI:	10.1016/j.cag.2022.07.004