TY - JOUR
T1 - Prior knowledge for part correspondence
AU - Van Kaick, Oliver
AU - Tagliasacchi, Andrea
AU - Sidi, Oana
AU - Zhang, Hao
AU - Cohen-Or, Daniel
AU - Wolf, Lior
AU - Hamarneh, Ghassan
PY - 2011
Y1 - 2011
N2 - Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves recognition of the shape parts where prior knowledge on the parts would play a more dominant role than geometric similarity. We introduce an approach to part correspondence which incorporates prior knowledge imparted by a training set of pre-segmented, labeled models and combines the knowledge with content-driven analysis based on geometric similarity between the matched shapes. First, the prior knowledge is learned from the training set in the form of per-label classifiers. Next, given two query shapes to be matched, we apply the classifiers to assign a probabilistic label to each shape face. Finally, by means of a joint labeling scheme, the probabilistic labels are used synergistically with pairwise assignments derived from geometric similarity to provide the resulting part correspondence. We show that the incorporation of knowledge is especially effective in dealing with shapes exhibiting large intra-class variations. We also show that combining knowledge and content analyses outperforms approaches guided by either attribute alone.
AB - Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves recognition of the shape parts where prior knowledge on the parts would play a more dominant role than geometric similarity. We introduce an approach to part correspondence which incorporates prior knowledge imparted by a training set of pre-segmented, labeled models and combines the knowledge with content-driven analysis based on geometric similarity between the matched shapes. First, the prior knowledge is learned from the training set in the form of per-label classifiers. Next, given two query shapes to be matched, we apply the classifiers to assign a probabilistic label to each shape face. Finally, by means of a joint labeling scheme, the probabilistic labels are used synergistically with pairwise assignments derived from geometric similarity to provide the resulting part correspondence. We show that the incorporation of knowledge is especially effective in dealing with shapes exhibiting large intra-class variations. We also show that combining knowledge and content analyses outperforms approaches guided by either attribute alone.
UR - http://www.scopus.com/inward/record.url?scp=82455199865&partnerID=8YFLogxK
U2 - 10.1111/j.1467-8659.2011.01893.x
DO - 10.1111/j.1467-8659.2011.01893.x
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AN - SCOPUS:82455199865
SN - 0167-7055
VL - 30
SP - 553
EP - 562
JO - Computer Graphics Forum
JF - Computer Graphics Forum
IS - 2
T2 - 32nd Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2011
Y2 - 11 April 2011 through 15 April 2011
ER -