Advertisement

SDNet: Semantically Guided Depth Estimation Network

  • Matthias OchsEmail author
  • Adrian Kretz
  • Rudolf Mester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)

Abstract

Autonomous vehicles and robots require a full scene understanding of the environment to interact with it. Such a perception typically incorporates pixel-wise knowledge of the depths and semantic labels for each image from a video sensor. Recent learning-based methods estimate both types of information independently using two separate CNNs. In this paper, we propose a model that is able to predict both outputs simultaneously, which leads to improved results and even reduced computational costs compared to independent estimation of depth and semantics. We also empirically prove that the CNN is capable of learning more meaningful and semantically richer features. Furthermore, our SDNet estimates the depth based on ordinal classification. On the basis of these two enhancements, our proposed method achieves state-of-the-art results in semantic segmentation and depth estimation from single monocular input images on two challenging datasets.

Notes

Acknowledgement

This project (HA project no. 626/18-49) is financed with funds of LOEWE – Landes-Offensive zur Entwicklung Wissenschaftlich-öko- nomischer Exzellenz, Förderlinie 3: KMU-Verbundvorhaben (State Offensive for the Development of Scientific and Economic Excellence).

We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

References

  1. 1.
    Alhaija, H., Mustikovela, S., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets computer vision: efficient data generation for urban driving scenes. Int. J. Comput. Vis. (IJCV) 126(9), 961–972 (2018)CrossRefGoogle Scholar
  2. 2.
    Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01234-2_49CrossRefGoogle Scholar
  3. 3.
    Cheng, J., Wang, Z., Pollastri, G.: A neural network approach to ordinal regression. In: International Joint Conference on Neural Networks, pp. 1279–1284 (2008)Google Scholar
  4. 4.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223 (2016)Google Scholar
  5. 5.
    Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems (NIPS), pp. 2366–2374 (2014)Google Scholar
  6. 6.
    Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2002–2011 (2018)Google Scholar
  7. 7.
    Garg, R., B.G., V.K., Carneiro, G., Reid, I.: Unsupervised CNN for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 740–756. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46484-8_45CrossRefGoogle Scholar
  8. 8.
    Gidaris, S., Komodakis, N.: Detect, replace, refine: deep structured prediction for pixel wise labeling. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7187–7196 (2017)Google Scholar
  9. 9.
    Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6602–6611 (2017)Google Scholar
  10. 10.
    Guo, X., Li, H., Yi, S., Ren, J., Wang, X.: Learning monocular depth by distilling cross-domain stereo networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 506–523. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01252-6_30CrossRefGoogle Scholar
  11. 11.
    Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(2), 328–341 (2008)CrossRefGoogle Scholar
  12. 12.
    Knöbelreiter, P., Reinbacher, C., Shekhovtsov, A., Pock, T.: End-to-end training of hybrid CNN-CRF models for stereo. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1456–1465 (2017)Google Scholar
  13. 13.
    Kong, S., Fowlkes, C.: Pixel-wise attentional gating for parsimonious pixel labeling. In: Winter Conference on Applications of Computer Vision (WACV) (2019)Google Scholar
  14. 14.
    Kuznietsov, Y., Stückler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2215–2223 (2017)Google Scholar
  15. 15.
    Ladický, L., Shi, J., Pollefeys, M.: Pulling things out of perspective. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 89–96 (2014)Google Scholar
  16. 16.
    Li, B., Dai, Y., He, M.: Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference. Pattern Recogn. 83, 328–339 (2018)CrossRefGoogle Scholar
  17. 17.
    Li, R., Xian, K., Shen, C., Cao, Z., Lu, H., Hang, L.: Deep attention-based classification network for robust depth prediction. CoRR arXiv, 1807.03959 [cs.CV] (2018)Google Scholar
  18. 18.
    Lin, T., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2018)Google Scholar
  19. 19.
    Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 38(10), 2024–2039 (2016)CrossRefGoogle Scholar
  20. 20.
    Liu, M., Salzmann, M., He, X.: Discrete-continuous depth estimation from a single image. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 716–723 (2014)Google Scholar
  21. 21.
    Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4040–4048 (2016)Google Scholar
  22. 22.
    Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4920–4928 (2016)Google Scholar
  23. 23.
    Pang, J., Sun, W., SJ. Ren, J., Yang, C., Yan, Q.: Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: International Conference on Computer Vision (ICCV) - Workshop, pp. 887–895 (2017)Google Scholar
  24. 24.
    Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: Advances in Neural Information Processing Systems (NIPS), pp. 1161–1168 (2005)Google Scholar
  25. 25.
    Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 31(5), 824–840 (2009)CrossRefGoogle Scholar
  26. 26.
    Schneider, L., et al.: Semantic stixels: depth is not enough. In: Intelligent Vehicles Symposium (IV), pp. 110–117 (2016)Google Scholar
  27. 27.
    Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant CNNs. In: International Conference on 3D Vision (3DV) (2017)Google Scholar
  28. 28.
    Žbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. (JMLR) 17, 2287–2318 (2016)zbMATHGoogle Scholar
  29. 29.
    Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01261-8_1CrossRefGoogle Scholar
  30. 30.
    Xie, J., Girshick, R., Farhadi, A.: Deep3D: fully automatic 2D-to-3D video conversion with deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 842–857. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_51CrossRefGoogle Scholar
  31. 31.
    Yang, G., Zhao, H., Shi, J., Deng, Z., Jia, J.: SegStereo: exploiting semantic information for disparity estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 660–676. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01234-2_39CrossRefGoogle Scholar
  32. 32.
    Yang, N., Wang, R., Stückler, J., Cremers, D.: Deep virtual stereo odometry: leveraging deep depth prediction for monocular direct sparse odometry. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 835–852. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01237-3_50CrossRefGoogle Scholar
  33. 33.
    Zhang, Z., Xu, C., Yang, J., Tai, Y., Chen, L.: Deep hierarchical guidance and regularization learning for end-to-end depth estimation. Pattern Recogn. 83, 430–442 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.VSI Lab, Goethe UniversityFrankfurt am MainGermany
  2. 2.Norwegian Open AI Lab, CS Dept. (IDI), NTNUTrondheimNorway

Personalised recommendations