Skip to main content

ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

Novel view synthesis is a long-standing problem. In this work, we consider a variant of the problem where we are given only a few context views sparsely covering a scene or an object. The goal is to predict novel viewpoints in the scene, which requires learning priors. The current state of the art is based on Neural Radiance Field (NeRF), and while achieving impressive results, the methods suffer from long training times as they require evaluating millions of 3D point samples via a neural network for each image. We propose a 2D-only method that maps multiple context views and a query pose to a new image in a single pass of a neural network. Our model uses a two-stage architecture consisting of a codebook and a transformer model. The codebook is used to embed individual images into a smaller latent space, and the transformer solves the view synthesis task in this more compact space. To train our model efficiently, we introduce a novel branching attention mechanism that allows us to use the same model not only for neural rendering but also for camera pose estimation. Experimental results on real-world scenes show that our approach is competitive compared to NeRF-based methods while not reasoning explicitly in 3D, and it is faster to train.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Notes

  1. 1.

    We tried dynamic weighting as described in [29], but it performed worse.

References

  1. Balntas, V., Li, S., Prisacariu, V.: RelocNet: continuous metric learning relocalisation using neural nets. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 751–767 (2018)

    Google Scholar 

  2. Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5855–5864 (2021)

    Google Scholar 

  3. Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)

  4. Bhayani, S., Sattler, T., Barath, D., Beliansky, P., Heikkilä, J., Kukelova, Z.: Calibrated and partially calibrated semi-generalized homographies. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  5. Blanton, H., Greenwell, C., Workman, S., Jacobs, N.: Extending absolute pose regression to multiple scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–39 (2020)

    Google Scholar 

  6. Brachmann, E., Rother, C.: Visual camera re-localization from RGB and RGB-D images using DSAC. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5847–5865 (2021)

    Google Scholar 

  7. Brachmann, E., Rother, C.: Visual camera re-localization from RGB and RGB-D images using DSAC. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5847–5865 (2021)

    Google Scholar 

  8. Brahmbhatt, S., Gu, J., Kim, K., Hays, J., Kautz, J.: Geometry-aware learning of maps for camera localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2616–2625 (2018)

    Google Scholar 

  9. Camposeco, F., Cohen, A., Pollefeys, M., Sattler, T.: Hybrid camera pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 136–144 (2018)

    Google Scholar 

  10. Cao, S., Snavely, N.: Graph-based discriminative learning for location recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 700–707 (2013)

    Google Scholar 

  11. Cavallari, T., et al.: Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2465–2477 (2019)

    Article  Google Scholar 

  12. Chan, S., Shum, H.Y., Ng, K.T.: Image-based rendering and synthesis. IEEE Signal Process. Mag. 24(6), 22–33 (2007)

    Article  Google Scholar 

  13. Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)

  14. Chen, S., Wang, Z., Prisacariu, V.: Direct-PoseNet: absolute pose regression with photometric consistency. arXiv preprint arXiv:2104.04073 (2021)

  15. Chen, W.C., Hu, M.C., Chen, C.S.: STR-GQN: Scene representation and rendering for unknown cameras based on spatial transformation routing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5966–5975 (2021)

    Google Scholar 

  16. Choi, I., Gallo, O., Troccoli, A., Kim, M.H., Kautz, J.: Extreme view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7781–7790 (2019)

    Google Scholar 

  17. Derner, E., Gomez, C., Hernandez, A.C., Barber, R., Babuška, R.: Change detection using weighted features for image-based localization. Robot. Auton. Syst. 135, 103676 (2021)

    Google Scholar 

  18. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis (2019)

    Google Scholar 

  19. Ding, M., Wang, Z., Sun, J., Shi, J., Luo, P.: CamNet: coarse-to-fine retrieval for camera re-localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2871–2880 (2019)

    Google Scholar 

  20. Eslami, S.A., et al.: Neural scene representation and rendering. Science 360(6394), 1204–1210 (2018)

    Article  Google Scholar 

  21. Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873–12883 (2021)

    Google Scholar 

  22. Garbin, S.J., Kowalski, M., Johnson, M., Shotton, J., Valentin, J.: FastNeRF: high-fidelity neural rendering at 200FPS. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14346–14355 (2021)

    Google Scholar 

  23. Glocker, B., Izadi, S., Shotton, J., Criminisi, A.: Real-time RGB-D camera relocalization. In: 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 173–179. IEEE (2013)

    Google Scholar 

  24. Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G., Brostow, G.: Deep blending for free-viewpoint image-based rendering. ACM Trans. Graph. (TOG) 37(6), 1–15 (2018)

    Article  Google Scholar 

  25. Henzler, P., et al.: Unsupervised learning of 3D object categories from videos in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4700–4709 (2021)

    Google Scholar 

  26. Irschara, A., Zach, C., Frahm, J.M., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2599–2606. IEEE (2009)

    Google Scholar 

  27. Jain, A., Tancik, M., Abbeel, P.: Putting NeRF on a diet: Semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5885–5894 (2021)

    Google Scholar 

  28. Kendall, A., Cipolla, R.: Modelling uncertainty in deep learning for camera relocalization. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 4762–4769. IEEE (2016)

    Google Scholar 

  29. Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5974–5983 (2017)

    Google Scholar 

  30. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2938–2946 (2015)

    Google Scholar 

  31. Laskar, Z., Melekhov, I., Kalia, S., Kannala, J.: Camera relocalization by computing pairwise relative poses using convolutional neural network. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 929–938 (2017)

    Google Scholar 

  32. Li, W., et al.: InteriorNet: mega-scale multi-sensor photo-realistic indoor scenes dataset. In: British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  33. Li, X., Ling, H.: TransCamP: graph transformer for 6-DoF camera pose estimation. ArXiv abs/2105.14065 (2021)

    Google Scholar 

  34. Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_2

    Chapter  Google Scholar 

  35. 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 

  36. Liu, L., Gu, J., Zaw Lin, K., Chua, T.S., Theobalt, C.: Neural sparse voxel fields. Adv. Neural Inf. Process. Syst. 33, 15651–15663 (2020)

    Google Scholar 

  37. Lynen, S., et al.: Large-scale, real-time visual-inertial localization revisited. Int. J. Robot. Res. 39(9), 1061–1084 (2020)

    Article  Google Scholar 

  38. Martin-Brualla, R., Radwan, N., Sajjadi, M.S., Barron, J.T., Dosovitskiy, A., Duckworth, D.: NeRF in the wild: neural radiance fields for unconstrained photo collections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7210–7219 (2021)

    Google Scholar 

  39. Melekhov, I., Ylioinas, J., Kannala, J., Rahtu, E.: Relative camera pose estimation using convolutional neural networks. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2017. LNCS, vol. 10617, pp. 675–687. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70353-4_57

    Chapter  Google Scholar 

  40. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24

    Chapter  Google Scholar 

  41. Moreau, A., Piasco, N., Tsishkou, D., Stanciulescu, B., de La Fortelle, A.: LENS: localization enhanced by NeRF synthesis. In: 5th Annual Conference on Robot Learning (2021)

    Google Scholar 

  42. Mueller, M.S., Sattler, T., Pollefeys, M., Jutzi, B.: Image-to-image translation for enhanced feature matching, image retrieval and visual localization. ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 4, 111–119 (2019)

    Article  Google Scholar 

  43. Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. arXiv preprint arXiv:2201.05989 (2022)

  44. Ng, T., Lopez-Rodriguez, A., Balntas, V., Mikolajczyk, K.: Reassessing the limitations of CNN methods for camera pose regression. arXiv:2108.07260 (2021)

  45. van den Oord, A., Vinyals, O., kavukcuoglu, k.: Neural discrete representation learning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

    Google Scholar 

  46. Parmar, N., et al.: Image transformer. In: International Conference on Machine Learning, pp. 4055–4064. PMLR (2018)

    Google Scholar 

  47. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)

    Google Scholar 

  48. Ramesh, A., et al.: Zero-shot text-to-image generation. arXiv preprint arXiv:2102.12092 (2021)

  49. Razavi, A., van den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)

    Google Scholar 

  50. Reiser, C., Peng, S., Liao, Y., Geiger, A.: KiloNeRF: speeding up neural radiance fields with thousands of tiny MLPs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14335–14345 (2021)

    Google Scholar 

  51. Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3D: large-scale learning and evaluation of real-life 3D category reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10901–10911 (2021)

    Google Scholar 

  52. Riegler, G., Koltun, V.: Free view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 623–640. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58529-7_37

    Chapter  Google Scholar 

  53. Riegler, G., Koltun, V.: Stable view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12216–12225 (2021)

    Google Scholar 

  54. Rombach, R., Esser, P., Ommer, B.: Geometry-free view synthesis: transformers and no 3D priors. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14356–14366 (2021)

    Google Scholar 

  55. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PIFu: pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2304–2314 (2019)

    Google Scholar 

  56. Sarlin, P.E., Cadena, C., Siegwart, R., Dymczyk, M.: From coarse to fine: robust hierarchical localization at large scale. In: CVPR (2019)

    Google Scholar 

  57. Sarlin, P.E., et al.: Back to the feature: learning robust camera localization from pixels to pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3247–3257 (2021)

    Google Scholar 

  58. Sattler, T., Leibe, B., Kobbelt, L.: Efficient & effective prioritized matching for large-scale image-based localization. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1744–1756 (2016)

    Article  Google Scholar 

  59. Sattler, T., Zhou, Q., Pollefeys, M., Leal-Taixe, L.: Understanding the limitations of CNN-based absolute camera pose regression. In: Proceedings of the IEEE/CVF Conference On computer Vision and Pattern Recognition, pp. 3302–3312 (2019)

    Google Scholar 

  60. Shavit, Y., Ferens, R., Keller, Y.: Learning multi-scene absolute pose regression with transformers. arXiv preprint arXiv:2103.11468 (2021)

  61. Shepard, R.N., Metzler, J.: Mental rotation of three-dimensional objects. Science 171(3972), 701–703 (1971)

    Article  Google Scholar 

  62. Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in RGB-D images. In: CVPR (2013)

    Google Scholar 

  63. Shum, H., Kang, S.B.: Review of image-based rendering techniques. In: Visual Communications and Image Processing 2000, vol. 4067, pp. 2–13. International Society for Optics and Photonics (2000)

    Google Scholar 

  64. Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: continuous 3D-structure-aware neural scene representations. Adv. Neural Inf. Process. Syst. 32, 1121–1132 (2019)

    Google Scholar 

  65. Thies, J., Zollhöfer, M., Theobalt, C., Stamminger, M., Nießner, M.: Image-guided neural object rendering. In: 8th International Conference on Learning Representations. OpenReview. net (2020)

    Google Scholar 

  66. Tobin, J., Zaremba, W., Abbeel, P.: Geometry-aware neural rendering. Adv. Neural. Inf. Process. Syst. 32, 11559–11569 (2019)

    Google Scholar 

  67. Trevithick, A., Yang, B.: GRF: learning a general radiance field for 3D representation and rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15182–15192 (2021)

    Google Scholar 

  68. Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems, pp. 5998–6008 (2017)

    Google Scholar 

  69. Wang, Q., et al.: IBRNET: learning multi-view image-based rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2021)

    Google Scholar 

  70. Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: iNeRF: inverting neural radiance fields for pose estimation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)

    Google Scholar 

  71. Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: PlenOctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5752–5761 (2021)

    Google Scholar 

  72. Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: neural radiance fields from one or few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4578–4587 (2021)

    Google Scholar 

  73. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  74. Zhang, W., Kosecka, J.: Image based localization in urban environments. In: Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT 2006), pp. 33–40. IEEE (2006)

    Google Scholar 

  75. Zhou, Q., Sattler, T., Pollefeys, M., Leal-Taixé, L.: To learn or not to learn: visual localization from essential matrices. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3319–3326 (2020)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the European Regional Development Fund under projects IMPACT (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000468) and Robotics for Industry 4.0 (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000470), the EU Horizon 2020 project RICAIP (grant agreement No 857306), the Grant Agency of the Czech Technical University in Prague (grant no. SGS22/112/OHK3/2T/13), and the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonáš Kulhánek .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 4821 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kulhánek, J., Derner, E., Sattler, T., Babuška, R. (2022). ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19784-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19783-3

  • Online ISBN: 978-3-031-19784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics