Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Multi-view based neural network for semantic segmentation on 3D scenes

This is a preview of subscription content, log in to check access.

References

  1. 1

    Wang J L, Lu Y H, Liu J B, et al. A robust three-stage approach to large-scale urban scene recognition. Sci China Inf Sci, 2017, 60: 103101

  2. 2

    Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3431–3440

  3. 3

    Kalogerakis E, Averkiou M, Maji S, et al. 3D shape segmentation with projective convolutional networks. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 6630–6639

  4. 4

    Kalogerakis E, Hertzmann A, Singh K. Learning 3D mesh segmentation and labeling. ACM Trans Graph, 2010, 29: 102

  5. 5

    Dai A, Chang A X, Savva M, et al. Scannet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2432–2443

  6. 6

    Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV 2015), 2015. 1520–1528

  7. 7

    He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016. 770–778

  8. 8

    Riemenschneider H, Bodis-Szomoru A, Weissenberg J, et al. Learning where to classify in multi-view semantic segmentation. In: Proceedings of European Conference on Computer Vision (ECCV 2014), 2014. 516–532

  9. 9

    Gadde R, Jampani V, Marlet R. Efficient 2D and 3D facade segmentation using auto-context. 2016. ArXiv: 1606.06437

Download references

Acknowledgements

This work was supported by GRF (Grant No. 16203518), Hong Kong RGC (Grant Nos. 16208614, T22-603/15N), Hong Kong ITC (Grant No. PSKL12EG02), and National Basic Research Program of China (973 Program) (Grant No. 2012CB316300).

Author information

Correspondence to Mingmin Zhen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lu, Y., Zhen, M. & Fang, T. Multi-view based neural network for semantic segmentation on 3D scenes. Sci. China Inf. Sci. 62, 229101 (2019). https://doi.org/10.1007/s11432-018-9828-3

Download citation