Advertisement

Geometric Image Synthesis

  • Hassan Abu AlhaijaEmail author
  • Siva Karthik Mustikovela
  • Andreas Geiger
  • Carsten Rother
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.

Notes

Acknowledgments

This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 programme (grant No. 647769) and by the Heidelberg Collaboratory for Image Processing (HCI).

References

  1. 1.
    Abu Alhaija, H., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets deep learning for car instance segmentation in urban scenes. In: BMVC (2017)Google Scholar
  2. 2.
    Abu Alhaija, H., Mustikovela, S.K., Mescheder, L., Geiger, A., Rother, C.: Augmented reality meets computer vision: efficient data generation for urban driving scenes. IJCV 126, 961–972 (2018)CrossRefGoogle Scholar
  3. 3.
    Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: ICCV (2017)Google Scholar
  4. 4.
    Chen, W., et al.: Synthesizing training images for boosting human 3D pose estimation. In: 3DV (2016)Google Scholar
  5. 5.
    Cheung, E., Wong, T.K., Bera, A., Manocha, D.: STD-PD: generating synthetic training data for pedestrian detection in unannotated videos. arXiv:1707.09100 (2017)
  6. 6.
    Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: CVPR (2016)Google Scholar
  7. 7.
    Denton, E.L., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: NIPS (2015)Google Scholar
  8. 8.
    Dosovitskiy, A., Springenberg, J.T., Tatarchenko, M., Brox, T.: Learning to generate chairs, tables and cars with convolutional networks. PAMI 39(4), 692–705 (2017)Google Scholar
  9. 9.
    Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: ICCV (2017)Google Scholar
  10. 10.
    Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: CVPR (2016)Google Scholar
  11. 11.
    Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv:1508.06576 (2015)
  12. 12.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: CVPR (2012)Google Scholar
  13. 13.
    Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  14. 14.
    Guzmán-Rivera, A., Batra, D., Kohli, P.: Multiple choice learning: learning to produce multiple structured outputs. In: NIPS (2012)Google Scholar
  15. 15.
    Hattori, H., Boddeti, V.N., Kitani, K.M., Kanade, T.: Learning scene-specific pedestrian detectors without real data. In: CVPR (2015)Google Scholar
  16. 16.
    He, K., Gkioxari, G., Dollr, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2980–2988, October 2017.  https://doi.org/10.1109/ICCV.2017.322
  17. 17.
    Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37331-2_42CrossRefGoogle Scholar
  18. 18.
    Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  19. 19.
    Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., Vasudevan, R.: Driving in the matrix: can virtual worlds replace human-generated annotations for real world tasks? In: ICRA (2017)Google Scholar
  20. 20.
    Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR (2016)Google Scholar
  21. 21.
    Mayer, N., et al.: What makes good synthetic training data for learning disparity and optical flow estimation? arXiv:1801.06397 (2018)
  22. 22.
    McCormac, J., Handa, A., Leutenegger, S., Davison, A.J.: SceneNet RGB-D: can 5M synthetic images beat generic ImageNet pre-training on indoor segmentation? In: ICCV (2017)Google Scholar
  23. 23.
    Michel, F., et al.: Global hypothesis generation for 6D object pose estimation. CoRR abs/1612.02287 (2016). http://arxiv.org/abs/1612.02287
  24. 24.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
  25. 25.
    Richter, S.R., Hayder, Z., Koltun, V.: Playing for benchmarks. In: ICCV (2017)Google Scholar
  26. 26.
    Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102–118. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46475-6_7CrossRefGoogle Scholar
  27. 27.
    Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: learning deep 3D representations at high resolutions. In: CVPR (2017)Google Scholar
  28. 28.
    Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: CVPR (2016)Google Scholar
  29. 29.
    Roth, K., Lucchi, A., Nowozin, S., Hofmann, T.: Stabilizing training of generative adversarial networks through regularization. In: NIPS, pp. 2018–2028 (2017)Google Scholar
  30. 30.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33715-4_54CrossRefGoogle Scholar
  31. 31.
    de Souza, C.R., Gaidon, A., Cabon, Y., Peña, A.M.L.: Procedural generation of videos to train deep action recognition networks. arXiv:1612.00881 (2016)
  32. 32.
    Tremblay, J., et al.: Training deep networks with synthetic data: bridging the reality gap by domain randomization. arXiv preprint arXiv:1804.06516 (2018)
  33. 33.
    Tsirikoglou, A., Kronander, J., Wrenninge, M., Unger, J.: Procedural modeling and physically based rendering for synthetic data generation in automotive applications. arXiv:1710.06270 (2017)
  34. 34.
    Varol, G., et al.: Learning from synthetic humans. In: CVPR (2017)Google Scholar
  35. 35.
    Veeravasarapu, V.S.R., Rothkopf, C.A., Ramesh, V.: Model-driven simulations for deep convolutional neural networks. arXiv:1605.09582 (2016)
  36. 36.
    Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. arXiv:1711.11585 (2017)
  37. 37.
    Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: CVPR (2018)Google Scholar
  38. 38.
    Wang, X., Gupta, A.: Generative image modeling using style and structure adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 318–335. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_20CrossRefGoogle Scholar
  39. 39.
    Xu, W., Li, Y., Lu, C.: Generating instance segmentation annotation by geometry-guided GAN. arXiv:1801.08839 (2018)
  40. 40.
    Yang, Z., Liu, H., Cai, D.: On the diversity of realistic image synthesis. arXiv:1712.07329 (2017)
  41. 41.
    Zhang, Y., et al.: Physically-based rendering for indoor scene understanding using convolutional neural networks. In: CVPR (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hassan Abu Alhaija
    • 1
    Email author
  • Siva Karthik Mustikovela
    • 1
  • Andreas Geiger
    • 2
    • 3
  • Carsten Rother
    • 1
  1. 1.Visual Learning LabHeidelberg UniversityHeidelbergGermany
  2. 2.Autonomous Vision GroupMPI for Intelligent SystemsTübingenGermany
  3. 3.University of TübingenTübingenGermany

Personalised recommendations