Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality

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.

[1]  Wei Chen,et al.  Learning for Disparity Estimation Through Feature Constancy , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Qiong Yan,et al.  Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[3]  Yong-Sheng Chen,et al.  Pyramid Stereo Matching Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.