Open AccessFused voxel autoencoder for single image to 3D object reconstruction
Author(s) -
Guzel Turhan C.,
Bilge H.S.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3293
Subject(s) - autoencoder , computer science , artificial intelligence , computer vision , encoder , object (grammar) , voxel , image (mathematics) , 3d reconstruction , range (aeronautics) , generative grammar , segmentation , iterative reconstruction , generative model , pattern recognition (psychology) , deep learning , engineering , aerospace engineering , operating system
The encoder–decoder based models become popular for many computer vision problems from image generation to image segmentation tasks. Due to the impressive performance of these models and Generative Adversarial Networks on image generation, these models have also adopted to 3D domains for 3D reconstruction and generation tasks. In this Letter, the authors have also attempted to solve a single image to 3D reconstruction problem by a novel encoder–decoder based model that is based on a fusion of encoders with the weak‐supervision approach. Thus, they have focused on multi‐category models inspiring the outstanding single‐category models on literature for real‐world tasks. In the experiments, it is seen that the proposed model is capable of generating the 3D objects from a single image by benefiting a wide range of features with weak class annotations.