A comparison of methods for 3D scene shape retrieval

Hameed Abdul-Rashid, Juefei Yuan, Bo Li*, Yijuan Lu, Tobias Schreck, Song Bai, Xiang Bai, Ngoc-Minh Ngoc-Minh , Minh N. Do, Trong-Le Do, Anh-Duc Duong, Kai He, Xinwei He, Mike Holenderski, Dmitri Jarnikov, Tu-Khiem Le, Wenhui Li, Anan Liu, Xiaolong Liu, Vlado MenkovskiKhac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Nguyen, Weizhi Nie, Van-Tu Ninh, Perez Rey, Yuting Su, Vinh Ton-That, Minh-Triet Tran, Tianyang Wang, Shu Xiang, Shandian Zhe, Heyu Zhou, Yang Zhou, Zhichao Zhou

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

3D scene shape retrieval is a brand new but important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on 2D scene sketch-based and image-based 3D scene model retrieval have been organized by us in 2018 and 2019, respectively. In 2018, we built the first benchmark for each track which contains 2D and 3D scene data for ten (10) categories, while they share the same 3D scene target dataset. Four and five distinct 3D scene shape retrieval methods have competed with each other in these two contests, respectively. In 2019, to measure and compare the scalability performance of the participating and other promising Query-by-Sketch or Query-by-Image 3D scene shape retrieval methods, we built a much larger extended benchmark for each type of retrieval which has thirty (30) classes and organized two extended tracks. Again, two and three different 3D scene shape retrieval methods have contended in these two tracks, separately. To solicit state-of-the-art approaches, we perform a comprehensive comparison of all the above methods and an additional new retrieval methods by evaluating them on the two benchmarks. The benchmarks, evaluation results and tools are publicly available at our track websites (Yuan et al., 2019 [1]; Abdul-Rashid et al., 2019 [2]; Yuan et al., 2019 [3]; Abdul-Rashid et al., 2019 [4]), while code for the evaluated methods are also available: http://github.com/3DSceneRetrieval.

Originalspracheenglisch
Aufsatznummer103070
FachzeitschriftComputer Vision and Image Understanding
Jahrgang201
DOIs
PublikationsstatusVeröffentlicht - Dez 2020

ASJC Scopus subject areas

  • Software
  • !!Signal Processing
  • !!Computer Vision and Pattern Recognition

Fields of Expertise

  • Human- & Biotechnology

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