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Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22475–22488 | Cite as

RGB-D joint modelling with scene geometric information for indoor semantic segmentation

  • Hong Liu
  • Wenshan Wu
  • Xiangdong Wang
  • Yueliang Qian
Article
  • 85 Downloads

Abstract

This paper focuses on the problem of RGB-D semantic segmentation for indoor scenes. We introduce a novel gravity direction detection method based on vertical lines fitting combined 2D vision information and 3D geometric information to improve the original HHA depth encoding. Then to fuse two-stream networks of deep convolutional networks from RGB and depth encoding, we propose a joint modelling method by learning a weighted summing layer to fuse the prediction results. Finally, to refine the pixel-wise score maps, we adopt fully-connected CRF as a post-processing and propose a pairwise potential function combined normal kernel to explore geometric information. Experimental results show our proposed approach achieves state-of-the-art performance of RGB-D semantic segmentation on public dataset.

Keywords

RGB-D Gravity direction Semantic segmentation CRF 

Notes

Acknowledgments

This work is supported in part by Beijing Natural Science Foundation: 4142051.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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