Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information. Stereo matching is a computer vision based approach for 3-D scene reconstruction. In this paper, we explore an improved stereo matching network, SLED-Net, in which a Single Long Encoder-Decoder is proposed to replace the stacked hourglass network in PSM-Net for better contextual information learning. We compare SLED-Net to state-of-the-art methods recently published, and demonstrate its superior performance on Scene Flow and KITTI2015 test sets.
@article{arxiv.1902.06255,
title = {Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality},
author = {Guang-Yu Nie and Yun Liu and Cong Wang and Yue Liu and Yongtian Wang},
journal= {arXiv preprint arXiv:1902.06255},
year = {2019}
}
Comments
To be published in IEEE VR2019 Conference as a Poster