TY - JOUR
T1 - Indirect shape analysis for 3D shape retrieval
AU - Liu, Zhenbao
AU - Xie, Caili
AU - Bu, Shuhui
AU - Wang, Xiao
AU - Han, Junwei
AU - Lin, Hongwei
AU - Zhang, Hao
N1 - Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.
PY - 2015/2
Y1 - 2015/2
N2 - We introduce indirect shape analysis, or ISA, where a given shape is analyzed not based on geometric or topological features computed directly from the shape itself, but by studying how external agents interact with the shape. The potential benefits of ISA are two-fold. First, agent-object interactions often reveal an object's function, which plays a key role in shape understanding. Second, compared to direct shape analysis, ISA, which utilizes pre-selected agents, is less affected by imperfections of, or inconsistencies between, the geometry or topology of the analyzed shapes. We employ digital human models as the external agents and develop a prototype ISA scheme for 3D shape classification and retrieval. Given a 3D model M, we compute an ISA feature vector for M by encoding how well a selected set of human models, with functional poses, can be aligned to M so as to perform the intended functions. We demonstrate the discriminability of ISA features for 3D shape retrieval and compare to state-of-the-art methods.
AB - We introduce indirect shape analysis, or ISA, where a given shape is analyzed not based on geometric or topological features computed directly from the shape itself, but by studying how external agents interact with the shape. The potential benefits of ISA are two-fold. First, agent-object interactions often reveal an object's function, which plays a key role in shape understanding. Second, compared to direct shape analysis, ISA, which utilizes pre-selected agents, is less affected by imperfections of, or inconsistencies between, the geometry or topology of the analyzed shapes. We employ digital human models as the external agents and develop a prototype ISA scheme for 3D shape classification and retrieval. Given a 3D model M, we compute an ISA feature vector for M by encoding how well a selected set of human models, with functional poses, can be aligned to M so as to perform the intended functions. We demonstrate the discriminability of ISA features for 3D shape retrieval and compare to state-of-the-art methods.
KW - 3D shape retrieval
KW - Indirect shape analysis
KW - Interacting agent
UR - http://www.scopus.com/inward/record.url?scp=84908377288&partnerID=8YFLogxK
U2 - 10.1016/j.cag.2014.09.038
DO - 10.1016/j.cag.2014.09.038
M3 - 文章
AN - SCOPUS:84908377288
SN - 0097-8493
VL - 46
SP - 110
EP - 116
JO - Computers and Graphics (Pergamon)
JF - Computers and Graphics (Pergamon)
ER -