Skip to main content

Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Abstract

Objects class, depth, and shape are instantly reconstructed by a human looking at a 2D image. While modern deep models solve each of these challenging tasks separately, they struggle to perform simultaneous scene 3D reconstruction and segmentation. We propose a single shot image-to-semantic voxel model translation framework. We train a generator adversarially against a discriminator that verifies the object’s poses. Furthermore, trapezium-shaped voxels, volumetric residual blocks, and 2D-to-3D skip connections facilitate our model learning explicit reasoning about 3D scene structure. We collected a SemanticVoxels dataset with 116k images, ground-truth semantic voxel models, depth maps, and 6D object poses. Experiments on ShapeNet and our SemanticVoxels datasets demonstrate that our framework achieves and surpasses state-of-the-art in the reconstruction of scenes with multiple non-rigid objects of different classes. We made our model and dataset publicly available (http://www.zefirus.org/SSZ).

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22–29 October 2017, pp. 2107–2115 (2017)

    Google Scholar 

  2. 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_38. As references [2] and [75] are same, we have deleted the duplicate reference and renumbered accordingly. Please check and confirm.

    Chapter  Google Scholar 

  3. Xie, H., Yao, H., Sun, X., Zhou, S., Zhang, S.: Pix2Vox: context-aware 3D reconstruction from single and multi-view images. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

    Google Scholar 

  4. Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3D reconstruction. In Wallach, H., Larochelle, H., Beygelzimer, A., dÁlché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 492–502. Curran Associates, Inc. (2019)

    Google Scholar 

  5. Jackson, A.S., Manafas, C., Tzimiropoulos, G.: 3D human body reconstruction from a single image via volumetric regression. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11132, pp. 64–77. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11018-5_6

    Chapter  Google Scholar 

  6. Shin, D., Ren, Z., Sudderth, E.B., Fowlkes, C.C.: 3D scene reconstruction with multi-layer depth and epipolar transformers. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

    Google Scholar 

  7. Choy, C.B., Gwak, J., Savarese, S.: 4D spatio-temporal ConvNets: Minkowski convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 3075–3084 (2019)

    Google Scholar 

  8. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  9. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks, pp. 4510–4520 (2018)

    Google Scholar 

  10. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  11. 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: Advances in Neural Information Processing Systems, pp. 82–90 (2016)

    Google Scholar 

  12. 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_29

    Chapter  Google Scholar 

  13. Shin, D., Fowlkes, C., Hoiem, D.: Pixels, voxels, and views: a study of shape representations for single view 3D object shape prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  14. Kalogerakis, E., Averkiou, M., Maji, S., Chaudhuri, S.: 3D shape segmentation with projective convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  15. Zhu, R., Kiani Galoogahi, H., Wang, C., Lucey, S.: Rethinking reprojection: closing the loop for pose-aware shape reconstruction from a single image. In: The IEEE International Conference on Computer Vision (ICCV) (October 2017)

    Google Scholar 

  16. Leroy, V., Franco, J.-S., Boyer, E.: Shape reconstruction using volume sweeping and learned photoconsistency. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 796–811. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_48

    Chapter  Google Scholar 

  17. Sridhar, S., Rempe, D., Valentin, J., Sofien, B., Guibas, L.J.: Multiview aggregation for learning category-specific shape reconstruction. In Wallach, H., Larochelle, H., Beygelzimer, A., dÁlché Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 2351–2362. Curran Associates, Inc. (2019)

    Google Scholar 

  18. Insafutdinov, E., Dosovitskiy, A.: Unsupervised learning of shape and pose with differentiable point clouds. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 2802–2812. Curran Associates, Inc. (2018)

    Google Scholar 

  19. Jiang, L., Shi, S., Qi, X., Jia, J.: GAL: geometric adversarial loss for single-view 3D-object reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 820–834. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_49

    Chapter  Google Scholar 

  20. Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W.T., Tenenbaum, J.B.: MarrNet: 3D shape reconstruction via 2.5D sketches. In: Advances In Neural Information Processing Systems (2017)

    Google Scholar 

  21. Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  22. Li, K., Pham, T., Zhan, H., Reid, I.: Efficient dense point cloud object reconstruction using deformation vector fields. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 508–524. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_31

    Chapter  Google Scholar 

  23. Zhang, X., Zhang, Z., Zhang, C., Tenenbaum, J., Freeman, B., Wu, J.: Learning to reconstruct shapes from unseen classes. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 2257–2268. Curran Associates, Inc. (2018)

    Google Scholar 

  24. Yang, G., Cui, Y., Belongie, S., Hariharan, B.: Learning single-view 3D reconstruction with limited pose supervision. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 90–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_6

    Chapter  Google Scholar 

  25. Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3D human pose. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  26. Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: Multi-view supervision for single-view reconstruction via differentiable ray consistency. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  27. Zhou, Y., Tuzel, O.: Voxelnet: end-to-end learning for point cloud based 3D object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  28. Moon, G., Yong Chang, J., Mu Lee, K.: V2V-PoseNet: voxel-to-voxel prediction network for accurate 3D hand and human pose estimation from a single depth map. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  29. Sitzmann, V., Thies, J., Heide, F., Niessner, M., Wetzstein, G., Zollhofer, M.: DeepVoxels: Learning persistent 3D feature embeddings. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

    Google Scholar 

  30. Gadelha, M., Wang, R., Maji, S.: Shape reconstruction using differentiable projections and deep priors. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

    Google Scholar 

  31. Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: DeepHuman: 3D human reconstruction from a single image. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

    Google Scholar 

  32. Richter, S.R., Roth, S.: Matryoshka networks: predicting 3D geometry via nested shape layers. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 1936–1944 (2018)

    Google Scholar 

  33. Zhang, D., Han, J., Yang, Y., Huang, D.: Learning category-specific 3D shape models from weakly labeled 2D images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  34. Zheng, C., Cham, T.-J., Cai, J.: T\(^2\)Net: synthetic-to-realistic translation for solving single-image depth estimation tasks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 798–814. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_47

    Chapter  Google Scholar 

  35. Feng, M., Gilani, S.Z., Wang, Y., Mian, A.: 3D face reconstruction from light field images: a model-free approach. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 508–526. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_31

    Chapter  Google Scholar 

  36. Kumar, S., Dai, Y., Li, H.: Monocular dense 3D reconstruction of a complex dynamic scene from two perspective frames. In: The IEEE International Conference on Computer Vision (ICCV) (October 2017)

    Google Scholar 

  37. Zhan, H., et al.: Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  38. Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., Fan, X.: Accurate monocular 3D object detection via color-embedded 3D reconstruction for autonomous driving. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)

    Google Scholar 

  39. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976. IEEE (2017)

    Google Scholar 

  40. Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 386–402. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_23

    Chapter  Google Scholar 

  41. Shimada, S., Golyanik, V., Theobalt, C., Stricker, D.: IsMo-GAN: adversarial learning for monocular non-rigid 3D reconstruction. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)

    Google Scholar 

  42. Zhou, Y., et al.: HairNet: single-view hair reconstruction using convolutional neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 249–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_15

    Chapter  Google Scholar 

  43. Alp Guler, R., Trigeorgis, G., Antonakos, E., Snape, P., Zafeiriou, S., Kokkinos, I.: DenseReg: fully convolutional dense shape regression in-the-wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  44. Shi, Y., Xu, K., Nießner, M., Rusinkiewicz, S., Funkhouser, T.: PlaneMatch: patch coplanarity prediction for robust RGB-D reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 767–784. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_46

    Chapter  Google Scholar 

  45. Wu, J., Zhang, C., Zhang, X., Zhang, Z., Freeman, W.T., Tenenbaum, J.B.: Learning shape priors for single-view 3D completion and reconstruction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 673–691. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_40

    Chapter  Google Scholar 

  46. Liu, C., Yang, J., Ceylan, D., Yumer, E., Furukawa, Y.: PlaneNet: piece-wise planar reconstruction from a single RGB image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  47. Agudo, A., Pijoan, M., Moreno-Noguer, F.: Image collection pop-up: 3D reconstruction and clustering of rigid and non-rigid categories. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  48. Sinha, A., Unmesh, A., Huang, Q., Ramani, K.: SurfNet: generating 3D shape surfaces using deep residual networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  49. Richardson, E., Sela, M., Or-El, R., Kimmel, R.: Learning detailed face reconstruction from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  50. Dou, P., Shah, S.K., Kakadiaris, I.A.: End-to-end 3D face reconstruction with deep neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)

    Google Scholar 

  51. Tewari, A., et al.: MoFA: model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In: The IEEE International Conference on Computer Vision (ICCV) (October 2017)

    Google Scholar 

  52. Jackson, A.S., Bulat, A., Argyriou, V., Tzimiropoulos, G.: Large pose 3D face reconstruction from a single image via direct volumetric CNN regression. In: The IEEE International Conference on Computer Vision (ICCV) (October 2017)

    Google Scholar 

  53. Sela, M., Richardson, E., Kimmel, R.: Unrestricted facial geometry reconstruction using image-to-image translation. In: The IEEE International Conference on Computer Vision (ICCV) (October 2017)

    Google Scholar 

  54. Huang, S., Qi, S., Zhu, Y., Xiao, Y., Xu, Y., Zhu, S.-C.: Holistic 3D scene parsing and reconstruction from a single RGB image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 194–211. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_12

    Chapter  Google Scholar 

  55. Kundu, A., Li, Y., Rehg, J.M.: 3D-RCNN: instance-level 3D object reconstruction via render-and-compare. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)

    Google Scholar 

  56. Knyaz, V.A., Kniaz, V.V., Remondino, F.: Image-to-voxel model translation with conditional adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 601–618. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_37

    Chapter  Google Scholar 

  57. Kniaz, V.V., Moshkantsev, P.V., Mizginov, V.A.: Deep learning a single photo voxel model prediction from real and synthetic images. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds.) NEUROINFORMATICS 2019. SCI, vol. 856, pp. 3–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-30425-6_1

    Chapter  Google Scholar 

  58. Kniaz, V.V., Remondino, F., Knyaz, V.A.: Generative adversarial networks for single photo 3D reconstruction. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W9, pp. 403–408 (2019)

    Google Scholar 

  59. Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 432–448. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01228-1_26

    Chapter  Google Scholar 

  60. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)

    Google Scholar 

  61. Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 4510–4520 (2018)

    Google Scholar 

  62. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 6517–6525 (2017)

    Google Scholar 

  63. Caesar, H., et al.: nuScenes: A multimodal dataset for autonomous driving. arXiv preprint arXiv:1903.11027 (2019)

  64. Locher, A., Havlena, M., Van Gool, L.: Progressive structure from motion. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 22–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_2

    Chapter  Google Scholar 

  65. Mizginov, V.A., Kniaz, V.V.: Evaluating the accuracy of 3D object reconstruction from thermal images. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W18, pp. 129–134 (2019)

    Google Scholar 

  66. Sun, X., et al.: Pix3D: dataset and methods for single-image 3D shape modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  67. Wang, T., Liu, M., Zhu, J., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18–22 June 2018, pp. 8798–8807 (2018)

    Google Scholar 

  68. Kniaz, V.V., Knyaz, V.A., Remondino, F.: The point where reality meets fantasy: mixed adversarial generators for image splice detection. In: Advances in Neural Information Processing Systems: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8–14 December 2019, Vancouver, BC, Canada, vol. 32, pp. 215–226 (2019)

    Google Scholar 

  69. Kniaz, V.V., Knyaz, V.A., Hladůvka, J., Kropatsch, W.G., Mizginov, V.: ThermalGAN: multimodal color-to-thermal image translation for person re-identification in multispectral dataset. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11134, pp. 606–624. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11024-6_46

    Chapter  Google Scholar 

  70. Kniaz, V.V., Bordodymov, A.N.: Long wave infrared image colorization for person re-identification. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W12, pp. 111–116 (2019)

    Google Scholar 

  71. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  72. Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  73. Chang, A.X., Funkhouser, T.A., et al.: ShapeNet: An information-rich 3D model repository. CoRR abs/1512.03012 (2015)

    Google Scholar 

  74. Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the Computer Vision and Pattern Recognition (CVPR). IEEE (2017)

    Google Scholar 

  75. Garbade, M., Chen, Y., Sawatzky, J., Gall, J.: Two stream 3D semantic scene completion. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16–20 June 2019, pp. 416–425 (2019)

    Google Scholar 

  76. Paszke, A., et al.: Automatic differentiation in PyTorch (2017)

    Google Scholar 

  77. Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: a benchmark for 3D object detection in the wild. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2014)

    Google Scholar 

Download references

Acknowledgments

The reported study was funded by Russian Foundation for Basic Research (RFBR) according to the research project N\(\mathrm {^{o}}\) 17-29-04509.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vladimir A. Knyaz .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mov 25284 KB)

Supplementary material 2 (pdf 13700 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kniaz, V.V., Knyaz, V.A., Remondino, F., Bordodymov, A., Moshkantsev, P. (2020). Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58571-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58570-9

  • Online ISBN: 978-3-030-58571-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics