TY - GEN
T1 - Learning barycentric representations of 3D shapes for sketch-based 3D shape retrieval
AU - Xie, Jin
AU - Dai, Guoxian
AU - Zhu, Fan
AU - Fang, Yi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use two deep convolutional neural networks (CNNs) to extract deep features of sketches and 2D projections of 3D shapes. For 3D shapes, we then compute the Wasserstein barycenters of deep features of multiple projections to form a barycentric representation. Finally, by constructing a metric network, a discriminative loss is formulated on the Wasserstein barycenters of 3D shapes and sketches in the deep feature space to learn discriminative and compact 3D shape and sketch features for retrieval. The proposed method is evaluated on the SHREC'13 and SHREC'14 sketch track benchmark datasets. Compared to the state-of-the-art methods, our proposed method can significantly improve the retrieval performance.
AB - Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use two deep convolutional neural networks (CNNs) to extract deep features of sketches and 2D projections of 3D shapes. For 3D shapes, we then compute the Wasserstein barycenters of deep features of multiple projections to form a barycentric representation. Finally, by constructing a metric network, a discriminative loss is formulated on the Wasserstein barycenters of 3D shapes and sketches in the deep feature space to learn discriminative and compact 3D shape and sketch features for retrieval. The proposed method is evaluated on the SHREC'13 and SHREC'14 sketch track benchmark datasets. Compared to the state-of-the-art methods, our proposed method can significantly improve the retrieval performance.
UR - http://www.scopus.com/inward/record.url?scp=85044287997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044287997&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.385
DO - 10.1109/CVPR.2017.385
M3 - Conference contribution
AN - SCOPUS:85044287997
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 3615
EP - 3623
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -