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

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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Pages (from-to)32-49
Number of pages18
JournalComputers and Graphics (Pergamon)
Volume107
DOIs
StatePublished - Oct 2022
Externally publishedYes

Keywords

  • Fitting primitives
  • Geometric primitives
  • Primitive descriptors
  • Primitive recognition
  • SHREC

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