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A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model trained on existing data underperforms in some settings and propose to address this with a large-scale dataset of 3.2 million dense segments on 44,560 indoor and outdoor images, which is 23x more segments than existing data. Our data covers a more diverse set of scenes, objects, viewpoints and materials, and contains a more fair distribution of skin types. We show that a model trained on our data outperforms a state-of-the-art model across datasets and viewpoints. We propose a large-scale scene parsing benchmark and baseline of 0.729 per-pixel accuracy, 0.585 mean class accuracy and 0.420 mean IoU across 46 materials.

P. Upchurch and R. Niu—Equal Contribution.

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Notes

  1. 1.

    Our data is available at https://github.com/apple/ml-dms-dataset..

References

  1. Adelson, E.H.: On seeing stuff: the perception of materials by humans and machines. In: Human vision and electronic imaging VI, vol. 4299, pp. 1–12. SPIE (2001)

    Google Scholar 

  2. Bell, S., Upchurch, P., Snavely, N., Bala, K.: OpenSurfaces: a richly annotated catalog of surface appearance. ACM Trans. Graph. (TOG) 32(4), 1–17 (2013)

    Article  Google Scholar 

  3. Bell, S., Upchurch, P., Snavely, N., Bala, K.: Material recognition in the wild with the Materials in Context database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3479–3487 (2015)

    Google Scholar 

  4. Brandao, M., Shiguematsu, Y.M., Hashimoto, K., Takanishi, A.: Material recognition CNNs and hierarchical planning for biped robot locomotion on slippery terrain. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 81–88. IEEE (2016)

    Google Scholar 

  5. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Firness, Accountability and Transparency, pp. 77–91. PMLR (2018)

    Google Scholar 

  6. Caesar, H., Uijlings, J., Ferrari, V.: COCO-Stuff: Thing and stuff classes in context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1209–1218 (2018)

    Google Scholar 

  7. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  8. Chen, L., Tang, W., John, N.W., Wan, T.R., Zhang, J.J.: Context-aware mixed reality: a learning-based framework for semantic-level interaction. In: Computer Graphics Forum, vol. 39, pp. 484–496. Wiley Online Library (2020)

    Google Scholar 

  9. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  10. Fitzpatrick, T.B.: The validity and practicality of sun-reactive skin types I through VI. Arch. Dermatol. 124(6), 869–871 (1988)

    Article  Google Scholar 

  11. Gao, Y., Hendricks, L.A., Kuchenbecker, K.J., Darrell, T.: Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 536–543. IEEE (2016)

    Google Scholar 

  12. Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: Detectron (2018). https://github.com/facebookresearch/detectron

  13. 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 

  14. Hu, D., Bo, L., Ren, X.: Toward robust material recognition for everyday objects. In: BMVC, vol. 2, p. 6. Citeseer (2011)

    Google Scholar 

  15. Jia, M.: Fashionpedia: ontology, segmentation, and an attribute localization dataset. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 316–332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_19

    Chapter  Google Scholar 

  16. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

    Google Scholar 

  17. Krasin, I., et al.: OpenImages: a public dataset for large-scale multi-label and multi-class image classification (2017). https://storage.googleapis.com/openimages/web/index.html

  18. 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 

  19. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (2017)

    Google Scholar 

  20. Mei, H., et al.: Don’t hit me! glass detection in real-world scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3687–3696 (2020)

    Google Scholar 

  21. Murmann, L., Gharbi, M., Aittala, M., Durand, F.: A dataset of multi-illumination images in the wild. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4080–4089 (2019)

    Google Scholar 

  22. Ordonez, V., Deng, J., Choi, Y., Berg, A.C., Berg, T.L.: From large scale image categorization to entry-level categories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2768–2775 (2013)

    Google Scholar 

  23. Park, K., Rematas, K., Farhadi, A., Seitz, S.M.: PhotoShape: photorealistic materials for large-scale shape collections. ACM Trans. Graph. 37(6) (2018)

    Google Scholar 

  24. Patterson, G., Hays, J.: SUN attribute database: discovering, annotating, and recognizing scene attributes. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2751–2758. IEEE (2012)

    Google Scholar 

  25. Ritchie, J.B., Paulun, V.C., Storrs, K.R., Fleming, R.W.: Material perception for philosophers. Philos Compass 16(10), e12777 (2021)

    Article  Google Scholar 

  26. Roberts, M., et al.: Hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10912–10922 (2021)

    Google Scholar 

  27. Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1), 157–173 (2008)

    Article  Google Scholar 

  28. Sajjan, S., et al.: ClearGrasp: 3D shape estimation of transparent objects for manipulation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3634–3642. IEEE (2020)

    Google Scholar 

  29. Schissler, C., Loftin, C., Manocha, D.: Acoustic classification and optimization for multi-modal rendering of real-world scenes. IEEE Trans. Vis. Comput. Graph. 24(3), 1246–1259 (2017)

    Article  Google Scholar 

  30. Schwartz, G., Nishino, K.: Recognizing material properties from images. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 1981–1995 (2019)

    Article  Google Scholar 

  31. Sharan, L., Liu, C., Rosenholtz, R., Adelson, E.H.: Recognizing materials using perceptually inspired features. Int. J. Comput. Vis. 103(3), 348–371 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  32. Sharan, L., Rosenholtz, R., Adelson, E.H.: Accuracy and speed of material categorization in real-world images. J. Vis. 14(9), 12–12 (2014)

    Article  Google Scholar 

  33. Svanera, M., Muhammad, U.R., Leonardi, R., Benini, S.: Figaro, hair detection and segmentation in the wild. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 933–937. IEEE (2016)

    Google Scholar 

  34. Van Zuijlen, M.J., Lin, H., Bala, K., Pont, S.C., Wijntjes, M.W.: Materials in paintings (MIP): an interdisciplinary dataset for perception, art history, and computer vision. PLoS ONE 16(8), e0255109 (2021)

    Article  Google Scholar 

  35. Wang, T.-C., Zhu, J.-Y., Hiroaki, E., Chandraker, M., Efros, A.A., Ramamoorthi, R.: A 4D light-field dataset and CNN architectures for material recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 121–138. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_8

    Chapter  Google Scholar 

  36. Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., Bailey, J.: Symmetric cross entropy for robust learning with noisy labels. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 322–330 (2019)

    Google Scholar 

  37. Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)

    Google Scholar 

  38. Xue, J., Zhang, H., Dana, K.: Deep texture manifold for ground terrain recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 558–567 (2018)

    Google Scholar 

  39. Xue, J., Zhang, H., Dana, K., Nishino, K.: Differential angular imaging for material recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 764–773 (2017)

    Google Scholar 

  40. Yang, K., Qinami, K., Fei-Fei, L., Deng, J., Russakovsky, O.: Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 547–558 (2020)

    Google Scholar 

  41. Yang, X., Mei, H., Xu, K., Wei, X., Yin, B., Lau, R.W.: Where is my mirror? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8809–8818 (2019)

    Google Scholar 

  42. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (2016)

    Google Scholar 

  43. Zhao, C., Sun, L., Stolkin, R.: A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition. In: 2017 18th International Conference on Advanced Robotics (ICAR), pp. 75–82. IEEE (2017)

    Google Scholar 

  44. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

    Google Scholar 

  45. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2017)

    Article  Google Scholar 

  46. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  47. Semantic understanding of scenes through the ADE20K dataset. Int. J. Comput. Vis. 127(3), 302–321 (2019)

    Google Scholar 

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Acknowledgements

We thank Allison Vanderby, Hillary Strickland, Laura Snarr, Mya Exum, Subhash Sudan, Sneha Deshpande, and Doris Guo for their help with acquiring data; Richard Gass, Daniel Kurz and Selim Ben Himane for their support.

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Upchurch, P., Niu, R. (2022). A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing. 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 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_26

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