3D object completion via class-conditional generative adversarial network
Many robotic tasks require accurate shape models in order to properly grasp or interact with
objects. However, it is often the case that sensors produce incomplete 3D models due to
several factors such as occlusion or sensor noise. To address this problem, we propose a
semi-supervised method that can recover the complete the shape of a broken or incomplete
3D object model. We formulated a hybrid of 3D variational autoencoder (VAE) and
generative adversarial network (GAN) to recover the complete voxelized 3D object …
objects. However, it is often the case that sensors produce incomplete 3D models due to
several factors such as occlusion or sensor noise. To address this problem, we propose a
semi-supervised method that can recover the complete the shape of a broken or incomplete
3D object model. We formulated a hybrid of 3D variational autoencoder (VAE) and
generative adversarial network (GAN) to recover the complete voxelized 3D object …
[CITATION][C] 3D object completion via class-conditional generative adversarial network
M Wang, D Tao, B Huet - MultiMedia Modeling, 2014 - Springer
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