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3D Shape Reconstruction from Free-Hand Sketches

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13808))

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Abstract

Sketches are arguably the most abstract 2D representations of real-world objects. Although a sketch usually has geometrical distortion and lacks visual cues, humans can effortlessly envision a 3D object from it. This suggests that sketches encode the information necessary for reconstructing 3D shapes. Despite great progress achieved in 3D reconstruction from distortion-free line drawings, such as CAD and edge maps, little effort has been made to reconstruct 3D shapes from free-hand sketches. We study this task and aim to enhance the power of sketches in 3D-related applications such as interactive design and VR/AR games.

Unlike previous works, which mostly study distortion-free line drawings, our 3D shape reconstruction is based on free-hand sketches. A major challenge for free-hand sketch 3D reconstruction comes from the insufficient training data and free-hand sketch diversity, e.g. individualized sketching styles. We thus propose data generation and standardization mechanisms. Instead of distortion-free line drawings, synthesized sketches are adopted as input training data. Additionally, we propose a sketch standardization module to handle different sketch distortions and styles. Extensive experiments demonstrate the effectiveness of our model and its strong generalizability to various free-hand sketches. Our code is available.

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References

  1. Alexiadis, D.S., et al.: An integrated platform for live 3D human reconstruction and motion capturing. IEEE Trans. Circuits Syst. Video Technol. 27(4), 798–813 (2016)

    Article  Google Scholar 

  2. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

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

  4. 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 

  5. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  6. Dai, D., Van Gool, L.: Dark model adaptation: Semantic image segmentation from daytime to nighttime. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3819–3824. IEEE (2018)

    Google Scholar 

  7. Delanoy, J., Aubry, M., Isola, P., Efros, A.A., Bousseau, A.: 3D sketching using multi-view deep volumetric prediction. Proc. ACM Comput. Graph. Interact. Tech. 1(1), 1–22 (2018)

    Article  Google Scholar 

  8. Dibra, E., Jain, H., Oztireli, C., Ziegler, R., Gross, M.: Human shape from silhouettes using generative HKS descriptors and cross-modal neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  9. Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4), 44:1–44:10 (2012)

    Google Scholar 

  10. 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 (2017)

    Google Scholar 

  11. Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M.: Visual simultaneous localization and mapping: a survey. Artif. Intell. Rev. 43(1), 55–81 (2015)

    Article  Google Scholar 

  12. Gao, X., Wang, N., Tao, D., Li, X.: Face sketch-photo synthesis and retrieval using sparse representation. IEEE Trans. Circuits Syst. Video Technol. 22(8), 1213–1226 (2012)

    Article  Google Scholar 

  13. Ghosh, A., et al.: Interactive sketch & fill: multiclass sketch-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  14. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)

    Google Scholar 

  15. Google: The quick, draw! Dataset (2017). quickdraw.withgoogle.com/data

  16. Guillard, B., Remelli, E., Yvernay, P., Fua, P.: Sketch2Mesh: reconstructing and editing 3D shapes from sketches. arXiv preprint arXiv:2104.00482 (2021)

  17. Han, Z., Ma, B., Liu, Y.S., Zwicker, M.: Reconstructing 3D shapes from multiple sketches using direct shape optimization. IEEE Trans. Image Process. 29, 8721–8734 (2020)

    Article  MATH  Google Scholar 

  18. Häne, C., Tulsiani, S., Malik, J.: Hierarchical surface prediction for 3D object reconstruction. In: 2017 International Conference on 3D Vision. IEEE (2017)

    Google Scholar 

  19. He, X., Zhou, Y., Zhou, Z., Bai, S., Bai, X.: Triplet-center loss for multi-view 3D object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  20. Huang, H., Kalogerakis, E., Yumer, E., Mech, R.: Shape synthesis from sketches via procedural models and convolutional networks. IEEE Trans. Visual Comput. Graphics 23(8), 2003–2013 (2016)

    Article  Google Scholar 

  21. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  22. Kar, A., Häne, C., Malik, J.: Learning a multi-view stereo machine. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

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

  24. Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)

  25. Kolotouros, N., Pavlakos, G., Daniilidis, K.: Convolutional mesh regression for single-image human shape reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  26. Lei, J., Song, Y., Peng, B., Ma, Z., Shao, L., Song, Y.Z.: Semi-heterogeneous three-way joint embedding network for sketch-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 30(9), 3226–3237 (2019)

    Article  Google Scholar 

  27. Li, C., Pan, H., Liu, Y., Tong, X., Sheffer, A., Wang, W.: BendSketch: modeling freeform surfaces through 2D sketching. ACM Trans. Graph. 36(4), 1–14 (2017)

    Google Scholar 

  28. Li, C., Pan, H., Liu, Y., Tong, X., Sheffer, A., Wang, W.: Robust flow-guided neural prediction for sketch-based freeform surface modeling. ACM Trans. Graph. (TOG) 37(6), 1–12 (2018)

    Article  Google Scholar 

  29. Liu, R., Yu, Q., Yu, S.: An unpaired sketch-to-photo translation model. arXiv preprint arXiv:1909.08313 (2019)

  30. Lun, Z., Gadelha, M., Kalogerakis, E., Maji, S., Wang, R.: 3D shape reconstruction from sketches via multi-view convolutional networks. In: 2017 International Conference on 3D Vision (3DV), pp. 67–77. IEEE (2017)

    Google Scholar 

  31. Mandikal, P., Navaneet, K., Agarwal, M., Babu, R.V.: 3D-LMNet: latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image. arXiv preprint arXiv:1807.07796 (2018)

  32. Mandikal, P., Radhakrishnan, V.B.: Dense 3D point cloud reconstruction using a deep pyramid network. In: 2019 IEEE Winter Conference on Applications of Computer Vision. IEEE (2019)

    Google Scholar 

  33. Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N., Guibas, L.J.: StructureNet: hierarchical graph networks for 3D shape generation. ACM Trans. Graph. 38(6) (2019)

    Google Scholar 

  34. Nag, S., Adak, S., Das, S.: What’s there in the dark. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2996–3000. IEEE (2019)

    Google Scholar 

  35. Navaneet, K., Mandikal, P., Agarwal, M., Babu, R.V.: CapNet: continuous approximation projection for 3D point cloud reconstruction using 2D supervision. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8819–8826 (2019)

    Google Scholar 

  36. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

  37. Nozawa, N., Shum, H.P., Ho, E.S., Morishima, S.: Single sketch image based 3D car shape reconstruction with deep learning and lazy learning. In: VISIGRAPP (1: GRAPP), pp. 179–190 (2020)

    Google Scholar 

  38. Özyeşil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion*. Acta Numer. 26, 305–364 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  39. Pan, J., Han, X., Chen, W., Tang, J., Jia, K.: Deep mesh reconstruction from single RGB images via topology modification networks. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  40. Peng, C., Gao, X., Wang, N., Li, J.: Superpixel-based face sketch-photo synthesis. IEEE Trans. Circuits Syst. Video Technol. 27(2), 288–299 (2015)

    Article  Google Scholar 

  41. Poma, X.S., Riba, E., Sappa, A.: Dense extreme inception network: towards a robust CNN model for edge detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1923–1932 (2020)

    Google Scholar 

  42. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  43. Richter, S.R., Roth, S.: Discriminative shape from shading in uncalibrated illumination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  44. Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102–118. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_7

    Chapter  Google Scholar 

  45. Sangkloy, P., Burnell, N., Ham, C., Hays, J.: The sketchy database: learning to retrieve badly drawn bunnies. ACM Trans. Graph. 35(4), 1–12 (2016)

    Article  Google Scholar 

  46. Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. In: ACM SIGGRAPH 2006 Papers, pp. 533–540 (2006)

    Google Scholar 

  47. Shen, L., Lin, Z., Huang, Q.: Relay backpropagation for effective learning of deep convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 467–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_29

    Chapter  Google Scholar 

  48. Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: efficient convolutional architectures for high-resolution 3D outputs. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  49. Wang, F., Kang, L., Li, Y.: Sketch-based 3d shape retrieval using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  50. Wang, L., Qian, C., Wang, J., Fang, Y.: Unsupervised learning of 3D model reconstruction from hand-drawn sketches. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 1820–1828 (2018)

    Google Scholar 

  51. Wang, L., Qian, X., Zhang, X., Hou, X.: Sketch-based image retrieval with multi-clustering re-ranking. IEEE Trans. Circuits Syst. Video Technol. 30(12), 4929–4943 (2019)

    Article  Google Scholar 

  52. Wang, N., Gao, X., Sun, L., Li, J.: Anchored neighborhood index for face sketch synthesis. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2154–2163 (2017)

    Article  Google Scholar 

  53. 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 

  54. Witkin, A.P.: Recovering surface shape and orientation from texture. Artif. Intell. 17(1–3), 17–45 (1981)

    Article  MATH  Google Scholar 

  55. Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems (2016)

    Google Scholar 

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

    Google Scholar 

  57. Xu, B., Chang, W., Sheffer, A., Bousseau, A., McCrae, J., Singh, K.: True2Form: 3D curve networks from 2D sketches via selective regularization. Trans. Graph. 33(4) (2014). 2601097.2601128

    Google Scholar 

  58. Xu, P., et al.: Fine-grained instance-level sketch-based video retrieval. IEEE Trans. Circuits Syst. Video Technol. 31(5), 1995–2007 (2020)

    Article  MathSciNet  Google Scholar 

  59. Yang, B., Rosa, S., Markham, A., Trigoni, N., Wen, H.: Dense 3D object reconstruction from a single depth view. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2820–2834 (2018)

    Article  Google Scholar 

  60. Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: PointFlow: 3D point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  61. Yang, S., Wang, Z., Liu, J., Guo, Z.: Deep plastic surgery: robust and controllable image editing with human-drawn sketches. arXiv preprint arXiv:2001.02890 (2020)

  62. Yu, Q., Liu, F., Song, Y.Z., Xiang, T., Hospedales, T.M., Loy, C.C.: Sketch me that shoe. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  63. Zhang, S., Gao, X., Wang, N., Li, J.: Face sketch synthesis from a single photo-sketch pair. IEEE Trans. Circuits Syst. Video Technol. 27(2), 275–287 (2015)

    Article  Google Scholar 

  64. Zhang, S.H., Guo, Y.C., Gu, Q.W.: Sketch2Model: view-aware 3D modeling from single free-hand sketches. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6012–6021 (2021)

    Google Scholar 

  65. Zhang, Y., Liu, Z., Liu, T., Peng, B., Li, X.: RealPoint3D: an efficient generation network for 3D object reconstruction from a single image. IEEE Access 7, 57539–57549 (2019)

    Article  Google Scholar 

  66. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

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Authors and Affiliations

  1. UC Berkeley/ICSI, Berkeley, CA, USA

    Jiayun Wang, Jierui Lin, Qian Yu, Runtao Liu, Yubei Chen & Stella X. Yu

  2. Beihang University, Beijing, China

    Qian Yu

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  1. Jiayun Wang
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  2. Jierui Lin
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  3. Qian Yu
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  4. Runtao Liu
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  6. Stella X. Yu
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Correspondence to Jiayun Wang .

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Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Wang, J., Lin, J., Yu, Q., Liu, R., Chen, Y., Yu, S.X. (2023). 3D Shape Reconstruction from Free-Hand Sketches. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13808. Springer, Cham. https://doi.org/10.1007/978-3-031-25085-9_11

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