SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds. (October 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Computers & graphics. Volume 107(2022)
• Page Start: 32
• Page End: 49
• Publication Date: 2022-10
• Subjects: Geometric primitives -- Fitting primitives -- Primitive recognition -- Primitive descriptors -- SHREC; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2022.07.004
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: 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 recognizing 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. Graphical abstract: Highlights: Evaluation of methods for fitting and recognizing simple primitives in point clouds. 46, 925 3D point clouds, divided into a training set and a test set. The goal is to recognize a primitive type and provide its analytical representation. Performance is discussed by classification, recognition, and approximation measures.