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NeXT: Towards High Quality Neural Radiance Fields via Multi-skip Transformer

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Neural Radiance Fields (NeRF) methods show impressive performance for novel view synthesis by representing a scene via a neural network. However, most existing NeRF based methods, including its variants, treat each sample point individually as input, while ignoring the inherent relationships between adjacent sample points from the corresponding rays, thus hindering the reconstruction performance. To address this issue, we explore a brand new scheme, namely NeXT, introducing a multi-skip transformer to capture the rich relationships between various sample points in a ray-level query. Specifically, ray tokenization is proposed to represent each ray as a sequence of point embeddings which is taken as input of our proposed NeXT. In this way, relationships between sample points are captured via the built-in self-attention mechanism to promote the reconstruction. Besides, our proposed NeXT can be easily combined with other NeRF based methods to improve their rendering quality. Extensive experiments conducted on three datasets demonstrate that NeXT significantly outperforms all previous state-of-the-art work by a large margin. In particular, the proposed NeXT surpasses the strong NeRF baseline by 2.74 dB of PSNR on Blender dataset. The code is available at https://github.com/Crishawy/NeXT.

Y Wang and Y Li—Equal contribution.

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Notes

  1. 1.

    https://github.com/Crishawy/NeXT.

References

  1. Arandjelović, R., Zisserman, A.: Nerf in detail: learning to sample for view synthesis. arXiv preprint arXiv:2106.05264 (2021)

  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. Bi, S., et al.: Neural reflectance fields for appearance acquisition. arXiv preprint arXiv:2008.03824 (2020)

  4. Boss, M., Braun, R., Jampani, V., Barron, J.T., Liu, C., Lensch, H.: Nerd: neural reflectance decomposition from image collections. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12684–12694 (2021)

    Google Scholar 

  5. Bradbury, J., et al.: Jax: composable transformations of python+ numpy programs 2018. http://github.com/google/jax 4, 16 (2020)

  6. Buehler, C., Bosse, M., McMillan, L., Gortler, S., Cohen, M.: Unstructured lumigraph rendering. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 425–432 (2001)

    Google Scholar 

  7. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  8. Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: Pi-GAN: periodic implicit generative adversarial networks for 3d-aware image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5799–5809 (2021)

    Google Scholar 

  9. Dai, Z., Cai, B., Lin, Y., Chen, J.: UP-DETR: unsupervised pre-training for object detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1601–1610 (2021)

    Google Scholar 

  10. Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019)

  11. Davis, A., Levoy, M., Durand, F.: Unstructured light fields. In: Computer Graphics Forum, vol. 31, pp. 305–314. Wiley Online Library (2012)

    Google Scholar 

  12. Debevec, P.E., Taylor, C.J., Malik, J.: Modeling and rendering architecture from photographs: a hybrid geometry-and image-based approach. In: Proceedings of the 23rd annual conference on Computer Graphics and Interactive Techniques, pp. 11–20 (1996)

    Google Scholar 

  13. Deng, B., Barron, J.T., Srinivasan, P.P.: JaxNeRF: an efficient JAX implementation of NeRF (2020)

    Google Scholar 

  14. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  15. Dosovitskiy, A., et al.: An image is worth 16 x 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  16. Flynn, J., et al.: DeepView: view synthesis with learned gradient descent. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2367–2376 (2019)

    Google Scholar 

  17. Gafni, G., Thies, J., Zollhofer, M., Nießner, M.: Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8649–8658 (2021)

    Google Scholar 

  18. Gao, C., Shih, Y., Lai, W.S., Liang, C.K., Huang, J.B.: Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012.05903 (2020)

  19. 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 

  20. Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 43–54 (1996)

    Google Scholar 

  21. Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.: Baking neural radiance fields for real-time view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5875–5884 (2021)

    Google Scholar 

  22. 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 

  23. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  24. Levoy, M., Hanrahan, P.: Light field rendering. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 31–42 (1996)

    Google Scholar 

  25. Li, Y., et al.: TokenPose: learning keypoint tokens for human pose estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11313–11322 (2021)

    Google Scholar 

  26. Li, Z., Niklaus, S., Snavely, N., Wang, O.: Neural scene flow fields for space-time view synthesis of dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6498–6508 (2021)

    Google Scholar 

  27. Lindell, D.B., Martel, J.N., Wetzstein, G.: AutoInt: automatic integration for fast neural volume rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14556–14565 (2021)

    Google Scholar 

  28. 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 

  29. Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., Sheikh, Y.: Neural volumes: learning dynamic renderable volumes from images. arXiv preprint arXiv:1906.07751 (2019)

  30. Mildenhall, B., et al.: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. ACM Trans. Graph. (TOG) 38(4), 1–14 (2019)

    Article  Google Scholar 

  31. 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 

  32. Neff, T., et al.: Donerf: towards real-time rendering of neural radiance fields using depth oracle networks. arXiv e-prints pp. arXiv-2103 (2021)

    Google Scholar 

  33. Niemeyer, M., Geiger, A.: Giraffe: representing scenes as compositional generative neural feature fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11453–11464 (2021)

    Google Scholar 

  34. Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3504–3515 (2020)

    Google Scholar 

  35. Ost, J., Mannan, F., Thuerey, N., Knodt, J., Heide, F.: Neural scene graphs for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2856–2865 (2021)

    Google Scholar 

  36. Park, K., et al.: Nerfies: Deformable neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5865–5874 (2021)

    Google Scholar 

  37. Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-nerf: neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10318–10327 (2021)

    Google Scholar 

  38. Raj, A., et al.: Pva: pixel-aligned volumetric avatars. arXiv preprint arXiv:2101.02697 (2021)

  39. Rebain, D., Jiang, W., Yazdani, S., Li, K., Yi, K.M., Tagliasacchi, A.: Derf: decomposed radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14153–14161 (2021)

    Google Scholar 

  40. 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 

  41. Rematas, K., Martin-Brualla, R., Ferrari, V.: ShaRF: shape-conditioned radiance fields from a single view. arXiv preprint arXiv:2102.08860 (2021)

  42. 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 

  43. 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 

  44. Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: Graf: Generative radiance fields for 3d-aware image synthesis. Adv. Neural Inf. Process. Syst. 33, 20154–20166 (2020)

    Google Scholar 

  45. Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. Int. J. Comput. Vision 35(2), 151–173 (1999)

    Article  Google Scholar 

  46. Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhofer, M.: DeepVoxels: learning persistent 3d feature embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437–2446 (2019)

    Google Scholar 

  47. Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: continuous 3d-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  48. Srinivasan, P.P., Deng, B., Zhang, X., Tancik, M., Mildenhall, B., Barron, J.T.: NeRV: neural reflectance and visibility fields for relighting and view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7495–7504 (2021)

    Google Scholar 

  49. Srinivasan, P.P., Tucker, R., Barron, J.T., Ramamoorthi, R., Ng, R., Snavely, N.: Pushing the boundaries of view extrapolation with multiplane images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 175–184 (2019)

    Google Scholar 

  50. Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272 (2021)

    Google Scholar 

  51. Tancik, M., et al.: Learned initializations for optimizing coordinate-based neural representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2846–2855 (2021)

    Google Scholar 

  52. Tolstikhin, I.O., et al.: MLP-Mixer: an all-MLP architecture for vision. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  53. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)

    Google Scholar 

  54. 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 

  55. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing systems vol. 30 (2017)

    Google Scholar 

  56. Waechter, M., Moehrle, N., Goesele, M.: Let there be color! large-scale texturing of 3d reconstructions. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision - ECCV 2014, ECCV 2014. Lecture Notes in Computer Science, vol. 8693, pp. 836–850. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_54

  57. 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 

  58. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  59. Wood, D.N., et al.: Surface light fields for 3d photography. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 287–296 (2000)

    Google Scholar 

  60. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  61. Yang, S., Quan, Z., Nie, M., Yang, W.: Transpose: towards explainable human pose estimation by transformer. arXiv e-prints pp. arXiv-2012 (2020)

    Google Scholar 

  62. Yariv, L., et al.: Multiview neural surface reconstruction by disentangling geometry and appearance. Adv. Neural Inf. Process. Syst. 33, 2492–2502 (2020)

    Google Scholar 

  63. 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 

  64. 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 

  65. Yuan, L., et al.: Tokens-to-Token ViT: training vision transformers from scratch on ImageNet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 558–567 (2021)

    Google Scholar 

  66. Yuan, Y., et al.: HRformer: high-resolution transformer for dense prediction. arXiv preprint arXiv:2110.09408 (2021)

  67. Zhou, T., Tucker, R., Flynn, J., Fyffe, G., Snavely, N.: Stereo magnification: learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817 (2018)

  68. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant 62171248, and the PCNL KEY project (PCL2021A07).

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Authors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China

    Yunxiao Wang, Yanjie Li, Peidong Liu & Shu-Tao Xia

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Tao Dai

  3. Research Center of Artificial Intelligence, Peng Cheng Laboratory, Shenzhen, China

    Shu-Tao Xia

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  1. Yunxiao Wang
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  3. Peidong Liu
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  4. Tao Dai
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Correspondence to Tao Dai .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Wang, Y., Li, Y., Liu, P., Dai, T., Xia, ST. (2022). NeXT: Towards High Quality Neural Radiance Fields via Multi-skip Transformer. 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 13692. Springer, Cham. https://doi.org/10.1007/978-3-031-19824-3_5

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