Computer Science ›› 2021, Vol. 48 ›› Issue (9): 216-222.doi: 10.11896/jsjkx.200800203

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Real-time Binocular Depth Estimation Algorithm Based on Semantic Edge Drive

ZHANG Peng, WANG Xin-qing, XIAO Yi, DUAN Bao-guo, XU Hong-hui   

  1. Department of Mechanical Engineering,College of Field Engineering,Army Engineering University,Nanjing 210007,China
  • Received:2020-08-29 Revised:2020-09-08 Online:2021-09-15 Published:2021-09-10
  • About author:ZHANG Peng,born in 1995,postgra-duate.His main research interests include deep learning,computer vision and point cloud processing.
    WANG Xin-qing,born in 1963,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent signal processing and deep learning.
  • Supported by:
    National Natural Science Foundation of China(61671470),National Basic Research Program of China(2016YFC0802904) and China Postdoctoral Science Foundation (2017M623423)

Abstract: Aiming at the problem of ill-posed regions with blurred disparity edges,unsmooth disparity,discontinuous disparity of a single object,and holes in stereo matching,a lightweight real-time binocular depth estimation algorithm is proposed,which uses the semantic tags obtained by semantic segmentation of the scene graph and the edge detail images obtained by edge detection asauxi-liary loss,and the ground truth image as the main loss,to construct the joint loss function which can better supervise the generation of the disparity map.In addition,a lightweight feature extraction module is constructed to reduce the redundancy of the feature extraction stage,which can better simplify the feature extraction steps,and improve the real-time and lightness of the network.Finally,the idea of from coarse to fine is used to realize the gradual refinement process of the disparity map with fusion of low-resolution disparity map deformation and high-resolution feature map to generate disparity maps of different scales in stages,meanwhile,the detailed features are gradually enriched,thus obtaining the final accurate disparity map.The 3px error rate of 1.72% is obtained on the KITTI 2012 dataset,the Vintge error rate on the Middlebury 2014 dataset is 1.23%,the Playroom error rate is 2.23%,and the Recycle error rate is 1.65%.Meanwhile,the calculation time on the Scene Flow dataset reaches 0.76 s with 2.4 G memory occupation,which significantly improves the accuracy and computational efficiency of stereo matching algorithms in the ill-posed regions,meets the real-time requirements in engineering practice,and has important guiding significance for real-time 3D reconstruction tasks.

Key words: Edge extraction, End-to-end network, From coarse to fine, Semantic understanding, Stereo matching

CLC Number: 

  • TP391.41
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