Advertisement

Learning Free-Form Deformations for 3D Object Reconstruction

  • Dominic JackEmail author
  • Jhony K. Pontes
  • Sridha Sridharan
  • Clinton Fookes
  • Sareh Shirazi
  • Frederic Maire
  • Anders Eriksson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11362)

Abstract

Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing voxel-based representations. While these approaches benefit greatly from advances in computer vision by generalizing 2D convolutions to the 3D setting, they also have several considerable drawbacks. The computational complexity of voxel-encodings grows cubically with the resolution thus limiting such representations to low-resolution 3D reconstruction. In an attempt to solve this problem, point cloud representations have been proposed. Although point clouds are more efficient than voxel representations as they only cover surfaces rather than volumes, they do not encode detailed geometric information about relationships between points. In this paper we propose a method to learn free-form deformations (Ffd) for the task of 3D reconstruction from a single image. By learning to deform points sampled from a high-quality mesh, our trained model can be used to produce arbitrarily dense point clouds or meshes with fine-grained geometry. We evaluate our proposed framework on synthetic data and achieve state-of-the-art results on surface and volumetric metrics. We make our implementation publicly available (Tensorflow implementation available at github.com/jackd/template_ffd.).

References

  1. 1.
    Penner, E., Zhang, L.: Soft 3D reconstruction for view synthesis. ACM Trans. Graph. 36 (2017)CrossRefGoogle Scholar
  2. 2.
    Huang, Q., Wang, H., Koltun, V.: Single-view reconstruction via joint analysis of image and shape collections. ACM Trans. Graph. 34 (2015)Google Scholar
  3. 3.
    Maier, R., Kim, K., Cremers, D., Kautz, J., Nießner, M.: Intrinsic3D: high-quality 3D reconstruction by joint appearance and geometry optimization with spatially-varying lighting. In: ICCV (2017)Google Scholar
  4. 4.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  5. 5.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. TPAMI 35, 1915–1929 (2013)CrossRefGoogle Scholar
  6. 6.
    Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. TPAMI 31, 855–868 (2009)CrossRefGoogle Scholar
  7. 7.
    Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46484-8_38CrossRefGoogle Scholar
  8. 8.
    Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H.: Perspective transformer nets: learning single-view 3D object reconstruction without 3D supervision. In: NIPS (2016)Google Scholar
  9. 9.
    Qi, C.R., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view CNNs for object classification on 3D data. In: CVPR (2016)Google Scholar
  10. 10.
    Kar, A., Häne, C., Malik, J.: Learning a multi-view stereo machine. In: NIPS (2017)Google Scholar
  11. 11.
    Zhu, R., Galoogahi, H.K., Wang, C., Lucey, S.: Rethinking reprojection: closing the loop for pose-aware shape reconstruction from a single image. In: NIPS (2017)Google Scholar
  12. 12.
    Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W.T., Tenenbaum, J.B.: MarrNet: 3D shape reconstruction via 2.5D sketches. In: NIPS (2017)Google Scholar
  13. 13.
    Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: CVPR (2017)Google Scholar
  14. 14.
    Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)Google Scholar
  15. 15.
    Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)Google Scholar
  16. 16.
    Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3D object reconstruction. In: AAAI (2018)Google Scholar
  17. 17.
    Sederberg, T., Parry, S.: Free-form deformation of solid geometric models. In: SIGGRAPH (1986)Google Scholar
  18. 18.
    Ulusoy, A.O., Geiger, A., Black, M.J.: Towards probabilistic volumetric reconstruction using ray potential. In: 3DV (2015)Google Scholar
  19. 19.
    Wu, Z., Song, S., Khosla, A., Tang, X., Xiao, J.: 3D ShapeNets: a deep representation for volumetric shapes. In: CVPR (2015)Google Scholar
  20. 20.
    Cherabier, I., Häne, C., Oswald, M.R., Pollefeys, M.: Multi-label semantic 3D reconstruction using voxel blocks. In: 3DV (2016)Google Scholar
  21. 21.
    Sharma, A., Grau, O., Fritz, M.: VConv-DAE: deep volumetric shape learning without object labels. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 236–250. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49409-8_20CrossRefGoogle Scholar
  22. 22.
    Rezende, D.J., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: Unsupervised learning of 3D structure from images. In: NIPS (2016)Google Scholar
  23. 23.
    Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: Learning a predictable and generative vector representation for objects. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 484–499. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_29CrossRefGoogle Scholar
  24. 24.
    Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: NIPS (2016)Google Scholar
  25. 25.
    Liu, J., Yu, F., Funkhouser, T.A.: Interactive 3D modeling with a generative adversarial network. In: 3DV (2017)Google Scholar
  26. 26.
    Gwak, J., Choy, C.B., Garg, A., Chandraker, M., Savarese, S.: Weakly supervised generative adversarial networks for 3D reconstruction. In: 3DV (2017)Google 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.
    Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. In: SIGGRAPH (2017)Google Scholar
  29. 29.
    Häne, C., Tulsiani, S., Malik, J.: Hierarchical surface prediction for 3D object reconstruction. In: 3DV (2017)Google Scholar
  30. 30.
    Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. In: ICCV (2017)Google Scholar
  31. 31.
    Lun, Z., Gadelha, M., Kalogerakis, E., Maji, S., Wang, R.: 3D shape reconstruction from sketches via multi-view convolutional networks. In: 3DV (2017)Google Scholar
  32. 32.
    Sinha, A., Unmesh, A., Huang, Q., Ramani, K.: SurfNet: generating 3D shape surfaces using deep residual network. In: CVPR (2017)Google Scholar
  33. 33.
    Yumer, M.E., Mitra, N.J.: Learning semantic deformation flows with 3D convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 294–311. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46466-4_18CrossRefGoogle Scholar
  34. 34.
    Kong, C., Lin, C.H., Lucey, S.: Using locally corresponding CAD models for dense 3D reconstructions from a single image. In: CVPR (2017)Google Scholar
  35. 35.
    Pontes, J.K., Kong, C., Eriksson, A., Fookes, C., Sridharan, S., Lucey, S.: Compact model representation for 3D reconstruction. In: 3DV (2017)Google Scholar
  36. 36.
    Kurenkov, A., et al.: DeformNet: free-form deformation network for 3D shape reconstruction from a single image. Volume abs/1708.04672 (2017)Google Scholar
  37. 37.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40, 99–121 (2000)CrossRefGoogle Scholar
  38. 38.
    Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  39. 39.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)Google Scholar
  40. 40.
    Chang, A.X., et al.: ShapeNet: an Information-Rich 3D Model Repository. Technical report arXiv:1512.03012 [cs.GR] (2015)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dominic Jack
    • 1
    Email author
  • Jhony K. Pontes
    • 1
  • Sridha Sridharan
    • 1
  • Clinton Fookes
    • 1
  • Sareh Shirazi
    • 1
  • Frederic Maire
    • 1
  • Anders Eriksson
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia

Personalised recommendations