A Voxel Generator Based on Autoencoder

Abstract

In recent years, 3D models have been widely used in the virtual/augmented reality industry. The traditional way of constructing 3D models for real-world objects remains expensive and time-consuming. With the rapid development of graphics processors, many approaches based on deep learning models have been proposed to reduce the time and economic cost of the generation of 3D object models. However, the quality of the generated 3D object models leaves considerable room for improvement. Accordingly, we designed and implemented a voxel generator called VoxGen, based on the autoencoder framework. It consists of an encoder that extracts image features and a decoder that maps feature values to voxel models. The main characteristics of VoxGen are exploiting modified VGG16 and ResNet18 to enhance the effect of feature extraction and mixing the deconvolution layer with the convolution layer in the decoder to enhance the feature of generated voxels. Our experimental results show that VoxGen outperforms related approaches in terms of the volumetric intersection over union (IOU) values of generated voxels.