Fast Single-View 3D Object Reconstruction with Fine Details Through Dilated Downsample and Multi-Path Upsample Deep Neural Network

Abstract

Three-dimensional (3D) object reconstruction is among the most important research areas in the field of computer vision. Its purpose is to reconstruct the overall shape of an object from its twodimensional (2D) image. With the development of deep learning, many methods based on convolutional neural networks (CNNs) have been applied in related research.To achieve 3D shape reconstruction with low computation time, we focus on the commonly used method: single-image reconstruction. The main issue of using a single image as an input is that the reconstruction shape often lacks structural detail. To address this issue, we proposed two methods: the dilated downsample block and the multi-path upsample block. The dilated downsample block extracts more features and the multi-path upsample block uses the features in our architecture. Thereafter, we concatenate the encoder and decoder with corresponding layers to keep the image features in reconstruction process.Finally, we perform experiments on the dataset provided by Choy et al. Results show that our method achieves 67.7% intersection over-union (IoU) accuracy, 3.6% higher than state-of-the-art method, VTN. Compared to the PSVH method, our result achieves 71.4%, an increase of 3.4%. Our average reconstruction time is 13 ms, approximately 25 times faster than PSVH.