TY - GEN
T1 - 3DensiNet
T2 - 25th ACM International Conference on Multimedia, MM 2017
AU - Wang, Meng
AU - Wang, Lingjing
AU - Fang, Yi
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - 3D volumetric object generation/prediction from single 2D image is a quite challenging but meaningful task in 3D visual computing. In this paper, we propose a novel neural network architecture, named "3DensiNet", which uses density heat-map as an intermediate supervision tool for 2D-to-3D transformation. Specifically, we firstly present a 2D density heat-map to 3D volumetric object encoding-decoding network, which outperforms classical 3D autoencoder. Then we show that using 2D image to predict its density heat-map via a 2D to 2D encoding-decoding network is feasible. In addition, we leverage adversarial loss to fine tune our network, which improves the generated/predicted 3D voxel objects to be more similar to the ground truth voxel object. Experimental results on 3D volumetric prediction from 2D images demonstrates superior performance of 3DensiNet over other state-of-the-art techniques in handling 3D volumetric object generation/prediction from single 2D image.
AB - 3D volumetric object generation/prediction from single 2D image is a quite challenging but meaningful task in 3D visual computing. In this paper, we propose a novel neural network architecture, named "3DensiNet", which uses density heat-map as an intermediate supervision tool for 2D-to-3D transformation. Specifically, we firstly present a 2D density heat-map to 3D volumetric object encoding-decoding network, which outperforms classical 3D autoencoder. Then we show that using 2D image to predict its density heat-map via a 2D to 2D encoding-decoding network is feasible. In addition, we leverage adversarial loss to fine tune our network, which improves the generated/predicted 3D voxel objects to be more similar to the ground truth voxel object. Experimental results on 3D volumetric prediction from 2D images demonstrates superior performance of 3DensiNet over other state-of-the-art techniques in handling 3D volumetric object generation/prediction from single 2D image.
KW - 3D reconstruction
KW - 3D volumetric prediction
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85035201598&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035201598&partnerID=8YFLogxK
U2 - 10.1145/3123266.3123340
DO - 10.1145/3123266.3123340
M3 - Conference contribution
AN - SCOPUS:85035201598
T3 - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
SP - 961
EP - 969
BT - MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PB - Association for Computing Machinery, Inc
Y2 - 23 October 2017 through 27 October 2017
ER -