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SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results

  • Conference paper
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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Abstract

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organised as a workshop in conjunction with ECCV 2020. There are two complementary challenges, the first one on 3D human scans, and the second one on generic objects. Challenge 1 is further split into two tracks, focusing, first, on large body and clothing regions, and, second, on fine body details. A novel evaluation metric is proposed to quantify jointly the shape reconstruction, the texture reconstruction and the amount of completed data. Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks. The datasets are released to the scientific community. Moreover, an accompanying custom library of software routines is also released to the scientific community. It allows for processing 3D scans, generating partial data and performing the evaluation. Results of the competition, analysed in comparison to baselines, show the validity of the proposed evaluation metrics, and highlight the challenging aspects of the task and of the datasets. Details on the SHARP 2020 challenge can be found at https://cvi2.uni.lu/sharp2020/.

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References

  1. Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: Scape: shape completion and animation of people. In: ACM SIGGRAPH 2005 Papers, pp. 408–416 (2005)

    Google Scholar 

  2. Artec3D: viewshape: online repository of 3D scans (2020). https://viewshape.com/. Accessed 10 Sept 2020

  3. Bogo, F., Romero, J., Loper, M., Black, M.J.: Faust: dataset and evaluation for 3D mesh registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3794–3801 (2014)

    Google Scholar 

  4. Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Numerical Geometry of Non-Rigid Shapes. MCS. Springer, New York (2009). https://doi.org/10.1007/978-0-387-73301-2

    Book  MATH  Google Scholar 

  5. Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit feature networks for texture completion of 3D data. In: SHARP Workshop, ECCV (2020)

    Google Scholar 

  6. Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit functions in feature space for 3D shape reconstruction and completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6970–6981 (2020)

    Google Scholar 

  7. Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. In: Computer Graphics Forum, vol. 28, pp. 337–346. Wiley Online Library (2009)

    Google Scholar 

  8. Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aanæs, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 406–413 (2014)

    Google Scholar 

  9. Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10975–10985 (2019)

    Google Scholar 

  10. Pishchulin, L., Wuhrer, S., Helten, T., Theobalt, C., Schiele, B.: Building statistical shape spaces for 3D human modeling. Pattern Recogn. 67, 276–286 (2017)

    Article  Google Scholar 

  11. Robinette, K.M., Daanen, H., Paquet, E.: The caesar project: a 3-D surface anthropometry survey. In: Second International Conference on 3-D Digital Imaging and Modeling (Cat. No. PR00062), pp. 380–386. IEEE (1999)

    Google Scholar 

  12. Saint, A., Ahmed, E., Cherenkova, K., Gusev, G., Aouada, D., Ottersten, B., et al.: 3dbodytex: textured 3D body dataset. In: 2018 International Conference on 3D Vision (3DV), pp. 495–504. IEEE (2018)

    Google Scholar 

  13. Saint, A., Cherenkova, K., Gusev, G., Aouada, D., Ottersten, B., et al.: Bodyfitr: robust automatic 3D human body fitting. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 484–488. IEEE (2019)

    Google Scholar 

  14. Saint, A., Kacem, A., Cherenkova, K., Aouada, D.: 3dbooster: 3D body shape and texture recovery. In: SHARP Workshop, ECCV (2020)

    Google Scholar 

  15. Saint, A., Shabayek, A.E.R., Aouada, D., Ottersten, B., Cherenkova, K., Gusev, G.: Towards automatic human body model fitting to a 3D scan. In: Proceedings of 3DBODY. TECH 2017–8th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11–12 October 2017, pp. 274–280. Hometrica Consulting (2017)

    Google Scholar 

  16. Xu, Z., Zhang, Q., Cheng, S.: Multilevel active registration for kinect human body scans: from low quality to high quality. Multimedia Syst. 24(3), 257–270 (2018)

    Article  Google Scholar 

  17. Yang, Y., Yu, Y., Zhou, Y., Du, S., Davis, J., Yang, R.: Semantic parametric reshaping of human body models. In: 2014 2nd International Conference on 3D Vision, vol. 2, pp. 41–48. IEEE (2014)

    Google Scholar 

  18. Zayer, R., Lévy, B., Seidel, H.P.: Linear angle based parameterization (2007)

    Google Scholar 

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Acknowledgements

We thank Artec3D for sponsoring this challenge with cash prizes and releasing the data for the 3DObjectTex dataset. This work and the data collection of 3D human scans for 3DBodyTex.v2 were partly supported by the Luxembourg National Research Fund (FNR) (11806282 and 11643091). We also gratefully acknowledge the participation, at different times, of all members of the Computer Vision, Imaging and Machine Intelligence (CVI\(^2\)) Research Group at the SnT, University of Luxembourg, including the moderation of the workshop event by Renato Baptista and the support of Pavel Chernakov in the development of the evaluation software. Finally, we express our appreciation to all the reviewers of the workshop submissions.

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Saint, A. et al. (2020). SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12536. Springer, Cham. https://doi.org/10.1007/978-3-030-66096-3_50

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  • DOI: https://doi.org/10.1007/978-3-030-66096-3_50

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