Single Image 3D Reconstruction Algorithm Based on Parallel Double-Branch Pyramid Stacking Network

Abstract

Limited visual information contained in single images and complex motion models of objects may lead to severe fragmentation and confusion of model backbone in the 3D reconstruction of objects. In order to fully extract feature information in a single image and reduce noise interference caused by environmental factors in 3D reconstruction, a Parallel Dual-Branch Pyramid Stacking Network (PDB-PSN) model is proposed. A parallel dual-branch network model is used to construct an encoder-decoder framework based on cascaded feature extraction, encode/decode high and low-resolution features in a single image, and then convert from 2D to 3D through implicit functions to achieve the 3D reconstruction of target objects in a single image. A cascaded feature extraction network is used as a low-resolution feature extraction network to extract global features of objects. In the high-resolution feature extraction branch, three concatenated hourglass networks and dilated convolutions are used in the hourglass network to increase the receptive field and obtain more global information in order to maintain the integrity of the reconstructed object limbs. A threshold processing module is set to remove irrelevant information and ensure the integrity of global information, and meanwhile, to reduce the interference of irrelevant noise information. Simulation experiments on a self-built terracotta dataset show that the PDB-PSN model can completely reconstruct the 3D model in a single image and effectively eliminate model fragmentation in the reconstruction results.