TY - JOUR
T1 - A comparison of methods for 3D scene shape retrieval
AU - Abdul-Rashid, Hameed
AU - Yuan, Juefei
AU - Li, Bo
AU - Lu, Yijuan
AU - Schreck, Tobias
AU - Bai, Song
AU - Bai, Xiang
AU - Ngoc-Minh , Ngoc-Minh
AU - Do, Minh N.
AU - Do, Trong-Le
AU - Duong, Anh-Duc
AU - He, Kai
AU - He, Xinwei
AU - Holenderski, Mike
AU - Jarnikov, Dmitri
AU - Le, Tu-Khiem
AU - Li, Wenhui
AU - Liu, Anan
AU - Liu, Xiaolong
AU - Menkovski, Vlado
AU - Nguyen, Khac-Tuan
AU - Nguyen, Thanh-An
AU - Nguyen, Vinh-Tiep
AU - Nie, Weizhi
AU - Ninh, Van-Tu
AU - Rey, Perez
AU - Su, Yuting
AU - Ton-That, Vinh
AU - Tran, Minh-Triet
AU - Wang, Tianyang
AU - Xiang, Shu
AU - Zhe, Shandian
AU - Zhou, Heyu
AU - Zhou, Yang
AU - Zhou, Zhichao
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - 3D scenes
KW - 3D shape retrieval
KW - Performance evaluation
KW - Query-by-Image
KW - Query-by-Sketch
KW - Scene benchmark
KW - Scene semantics
KW - Scene understanding
KW - SHREC
UR - http://www.scopus.com/inward/record.url?scp=85090050922&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2020.103070
DO - 10.1016/j.cviu.2020.103070
M3 - Article
SN - 1077-3142
VL - 201
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
M1 - 103070
ER -