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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 201–219Cite as

Directed Ray Distance Functions for 3D Scene Reconstruction

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13662))

Abstract

We present an approach for full 3D scene reconstruction from a single unseen image. We trained on dataset of realistic non-watertight scans of scenes. Our approach uses a predicted distance function, since these have shown promise in handling complex topologies and large spaces. We identify and analyze two key challenges for predicting such image conditioned distance functions that have prevented their success on real 3D scene data. First, we show that predicting a conventional scene distance from an image requires reasoning over a large receptive field. Second, we analytically show that the optimal output of the network trained to predict these distance functions does not obey all the distance function properties. We propose an alternate distance function, the Directed Ray Distance Function (DRDF), that tackles both challenges. We show that a deep network trained to predict DRDFs outperforms all other methods quantitatively and qualitatively on 3D reconstruction from single image on Matterport3D, 3DFront, and ScanNet. (Project Page: https://nileshkulkarni.github.io/scene_drdf)

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References

  1. Atzmon, M., Lipman, Y.: Sal: sign agnostic learning of shapes from raw data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2565–2574 (2020)

    Google Scholar 

  2. Atzmon, M., Lipman, Y.: Sal++: sign agnostic learning with derivatives. arXiv preprint arXiv:2006.05400 (2020)

  3. Bae, G., Budvytis, I., Cipolla, R.: Estimating and exploiting the aleatoric uncertainty in surface normal estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13137–13146 (2021)

    Google Scholar 

  4. Barrow, H., Tenenbaum, J., Hanson, A., Riseman, E.: Recovering intrinsic scene characteristics. Comput. Vis. Syst 2(3–26), 2 (1978)

    Google Scholar 

  5. Chang, A., et al.: Matterport3D: learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158 (2017)

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

  7. Chen, K., Snavely, N., Makadia, A.: Wide-baseline relative camera pose estimation with directional learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3258–3268 (2021)

    Google Scholar 

  8. Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5939–5948 (2019)

    Google Scholar 

  9. Chibane, J., Mir, A., Pons-Moll, G.: Neural unsigned distance fields for implicit function learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  10. 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

    Chapter  Google Scholar 

  11. 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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828–5839 (2017)

    Google Scholar 

  12. Del Pero, L., Bowdish, J., Kermgard, B., Hartley, E., Barnard, K.: Understanding bayesian rooms using composite 3D object models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)

    Google Scholar 

  13. Dhamo, H., Navab, N., Tombari, F.: Object-driven multi-layer scene decomposition from a single image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5369–5378 (2019)

    Google Scholar 

  14. Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605–613 (2017)

    Google Scholar 

  15. Fidler, S., Dickinson, S., Urtasun, R.: 3D object detection and viewpoint estimation with a deformable 3D cuboid model. In: Advances in Neural Information Processing Systems, pp. 611–619 (2012)

    Google Scholar 

  16. Fouhey, D.F., Gupta, A., Hebert, M.: Data-driven 3D primitives for single image understanding. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3392–3399 (2013)

    Google Scholar 

  17. Fu, H., et al.: 3D-front: 3D furnished rooms with layouts and semantics. arXiv preprint arXiv:2011.09127 (2020)

  18. 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 

  19. Gkioxari, G., Malik, J., Johnson, J.: Mesh r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9785–9795 (2019)

    Google Scholar 

  20. Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-mâché approach to learning 3D surface generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 216–224 (2018)

    Google Scholar 

  21. Häne, C., Tulsiani, S., Malik, J.: Hierarchical surface prediction for 3D object reconstruction. In: 2017 International Conference on 3D Vision (3DV), pp. 412–420. IEEE (2017)

    Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  23. Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1849–1856. IEEE (2009)

    Google Scholar 

  24. Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, pp. 654–661. IEEE (2005)

    Google Scholar 

  25. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)

    Google Scholar 

  26. Issaranon, T., Zou, C., Forsyth, D.: Counterfactual depth from a single rgb image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)

    Google Scholar 

  27. Izadinia, H., Shan, Q., Seitz, S.M.: Im2cad. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5134–5143 (2017)

    Google Scholar 

  28. Jiang, Z., Liu, B., Schulter, S., Wang, Z., Chandraker, M.: Peek-a-boo: occlusion reasoning in indoor scenes with plane representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113–121 (2020)

    Google Scholar 

  29. Jin, L., Qian, S., Owens, A., Fouhey, D.F.: Planar surface reconstruction from sparse views. In: International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  30. Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5580–5590 (2017)

    Google Scholar 

  31. Ku, J., Pon, A.D., Waslander, S.L.: Monocular 3D object detection leveraging accurate proposals and shape reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11867–11876 (2019)

    Google Scholar 

  32. Kulkarni, N., Gupta, A., Tulsiani, S.: Canonical surface mapping via geometric cycle consistency. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2202–2211 (2019)

    Google Scholar 

  33. Kulkarni, N., Misra, I., Tulsiani, S., Gupta, A.: 3D-relnet: joint object and relational network for 3d prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2212–2221 (2019)

    Google Scholar 

  34. Li, L., Khan, S., Barnes, N.: Silhouette-assisted 3D object instance reconstruction from a cluttered scene. In: ICCV Workshops (2019)

    Google Scholar 

  35. Lin, C.H., Kong, C., Lucey, S.: Learning efficient point cloud generation for dense 3d object reconstruction. arXiv preprint arXiv:1706.07036 (2017)

  36. 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. arXiv preprint arXiv:2008.02268 (2020)

  37. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4460–4470 (2019)

    Google Scholar 

  38. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934 (2020)

  39. Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: 3D bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 7074–7082 (2017)

    Google Scholar 

  40. Newbold, P.: Arima model building and the time series analysis approach to forecasting. J. Forecast. 2(1), 23–35 (1983)

    Article  Google Scholar 

  41. Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., Zhang, J.J.: Total3dunderstanding: joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 55–64 (2020)

    Google Scholar 

  42. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)

    Google Scholar 

  43. Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.: Convolutional occupancy networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 523–540. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_31

    Chapter  Google Scholar 

  44. Poggi, M., Aleotti, F., Tosi, F., Mattoccia, S.: On the uncertainty of self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3227–3237 (2020)

    Google Scholar 

  45. Raistrick, A., Kulkarni, N., Fouhey, D.F.: Collision replay: what does bumping into things tell you about scene geometry? arXiv preprint arXiv:2105.01061 (2021)

  46. Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2020)

    Google Scholar 

  47. 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 International Conference on Computer Vision, pp. 2304–2314 (2019)

    Google Scholar 

  48. Saito, S., Simon, T., Saragih, J., Joo, H.: Pifuhd: multi-level pixel-aligned implicit function for high-resolution 3D human digitization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 84–93 (2020)

    Google Scholar 

  49. Saxena, A., Sun, M., Ng, A.Y.: Make3d: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2008)

    Article  Google Scholar 

  50. Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 519–528. IEEE (2006)

    Google Scholar 

  51. Shade, J., Gortler, S., He, L.W., Szeliski, R.: Layered depth images. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 231–242 (1998)

    Google Scholar 

  52. Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Adv. Neural Inf. Process. Syst. 33 (2020)

    Google Scholar 

  53. Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1754 (2017)

    Google Scholar 

  54. Sun, X., et al.: Pix3d: dataset and methods for single-image 3D shape modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2974–2983 (2018)

    Google Scholar 

  55. Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3D reconstruction networks learn? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3405–3414 (2019)

    Google Scholar 

  56. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: a simple and strong anchor-free object detector. TPAMI 44, 1922–1933 (2020)

    Google Scholar 

  57. Tulsiani, S., Gupta, S., Fouhey, D.F., Efros, A.A., Malik, J.: Factoring shape, pose, and layout from the 2D image of a 3D scene. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 302–310 (2018)

    Google Scholar 

  58. Tulsiani, S., Su, H., Guibas, L.J., Efros, A.A., Malik, J.: Learning shape abstractions by assembling volumetric primitives. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2635–2643 (2017)

    Google Scholar 

  59. Tulsiani, S., Tucker, R., Snavely, N.: Layer-structured 3D scene inference via view synthesis. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 311–327. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_19

    Chapter  Google Scholar 

  60. Virtanen, P., et al.: SciPy 1.0 contributors: SciPy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2

  61. Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.-G.: Pixel2Mesh: generating 3D mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55–71. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_4

    Chapter  Google Scholar 

  62. Wang, X., Fouhey, D.F., Gupta, A.: Designing deep networks for surface normal estimation. In: CVPR (2015)

    Google Scholar 

  63. Weyn, J.A., Durran, D.R., Caruana, R.: Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. J. Adv. Model. Earth Syst. 12(9), e2020MS002109 (2020)

    Google Scholar 

  64. Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3D reconstruction. Adv. Neural Inf. Process. Syst., 492–502 (2019)

    Google Scholar 

  65. Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: neural radiance fields from one or few images. In: CVPR (2021)

    Google Scholar 

  66. Zhang, K., Riegler, G., Snavely, N., Koltun, V.: Nerf++: analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492 (2020)

  67. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_40

    Chapter  Google Scholar 

  68. Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)

  69. Zhu, S., Ebrahimi, S., Kanazawa, A., Darrell, T.: Differentiable gradient sampling for learning implicit 3D scene reconstructions from a single image. In: International Conference on Learning Representations (2021)

    Google Scholar 

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Acknowledgement

We would like the thank Alexandar Raistrick and Chris Rockwell for their help with the 3DFront dataset. We like to thank Shubham Tulsiani, Ekdeep Singh Lubana, Richard Higgins, Sarah Jabour, Shengyi Qian, Linyi Jin, Karan Desai, Mohammed El Banani, Chris Rockwell, Alexandar Raistrick, Dandan Shan, Andrew Owens for comments on the draft versions of this paper. NK was supported by TRI. Toyota Research Institute (“TRI”) provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, 48105, USA

    Nilesh Kulkarni, Justin Johnson & David F. Fouhey

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  1. Nilesh Kulkarni
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  2. Justin Johnson
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  3. David F. Fouhey
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Correspondence to Nilesh Kulkarni .

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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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Kulkarni, N., Johnson, J., Fouhey, D.F. (2022). Directed Ray Distance Functions for 3D Scene Reconstruction. 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 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_12

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