“Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks” by Huang, Kalogerakis, Chaudhuri, Ceylan and Kim

  • ©Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, and Vladimir G. Kim

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    Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks

Session/Category Title: Image & Shape Analysis With CNNs

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Abstract:


    We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching. The descriptor is produced by a convolutional network that is trained to embed geometrically and semantically similar points close to one another in descriptor space. The network processes surface neighborhoods around points on a shape that are captured at multiple scales by a succession of progressively zoomed-out views, taken from carefully selected camera positions. We leverage two extremely large sources of data to train our network. First, since our network processes rendered views in the form of 2D images, we repurpose architectures pretrained on massive image datasets. Second, we automatically generate a synthetic dense point correspondence dataset by nonrigid alignment of corresponding shape parts in a large collection of segmented 3D models. As a result of these design choices, our network effectively encodes multiscale local context and fine-grained surface detail. Our network can be trained to produce either category-specific descriptors or more generic descriptors by learning from multiple shape categories. Once trained, at test time, the network extracts local descriptors for shapes without requiring any part segmentation as input. Our method can produce effective local descriptors even for shapes whose category is unknown or different from the ones used while training. We demonstrate through several experiments that our learned local descriptors are more discriminative compared to state-of-the-art alternatives and are effective in a variety of shape analysis applications.

References:


    1. M. Ankerst, G. Kastenmüller, H.-P. Kriegel, and T. Seidl. 1999. 3D shape histograms for similarity search and classification in spatial databases. In Proceedings of the International Symposium on Advances in Spatial Databases. 207–226. Google ScholarDigital Library
    2. M. Aubry, U. Schlickewei, and D. Cremers. 2011. The wave kernel signature: A quantum mechanical approach to shape analysis. In 2011 IEEE International Conference on Computer Vision Workshops.Google Scholar
    3. S. Belongie, J. Malik, and J. Puzicha. 2002. Shape matching and object recognition using shape contexts. In IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 4 (2002), 509–522. Google ScholarDigital Library
    4. L. Bo, X. Ren, and D. Fox. 2014. Learning hierarchical sparse features for RGB-(D) object recognition. The International Journal of Robotics Research 33, 4 (2014), 581–599. Google ScholarDigital Library
    5. F. Bogo, J. Romero, M. Loper, and M. J. Black. 2014. FAUST: Dataset and evaluation for 3D mesh registration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). Google ScholarDigital Library
    6. D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, and P. Vandergheynst. 2015. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Proceedings of the Symposium on Geometry Processing (SGP’15). 13–23.Google Scholar
    7. D. Boscaini, J. Masci, E. Rodol, and M. M. Bronstein. 2016. Learning shape correspondence with anisotropic convolutional neural networks. The Conference and Workshop on Neural Information Processing Systems (NIPS’16). Google ScholarDigital Library
    8. J. Bromley, I. Guyon, Y. Lecun, E. Sackinger, and R. Shah. 1994. Signature Verification using a Siamese Time Delay Neural Network. Advances in Neural Information Processing Systems 6. Morgan-Kaufmann. 737–744. Google ScholarDigital Library
    9. A. M. Bronstein, M. M. Bronstein, L. J. Guibas, and M. Ovsjanikov. 2011. Shape Google: Geometric words and expressions for invariant shape retrieval. ACM Transactions on Graphics 30, 1 (2011), 1:1–1:20. Google ScholarDigital Library
    10. A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q.-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu. 2015. ShapeNet: An information-rich 3D model repository. CoRR.Google Scholar
    11. D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung. 2003. On visual similarity based 3D model retrieval. Computer Graphics Forum 22, 3 (2003), 223–232.Google ScholarCross Ref
    12. H. Fu, D. Cohen-Or, G. Dror, and A. Sheffer. 2008. Upright orientation of man-made objects. ACM Trans. Graph. 27, 3 (2008). Google ScholarDigital Library
    13. R. Gal and D. Cohen-Or. 2006. Salient geometric features for partial shape matching and similarity. ACM Transactions on Graphics 25, 1 (2006), 130–150. Google ScholarDigital Library
    14. K. Guo, D. Zou, and X. Chen. 2015. 3D mesh labeling via deep convolutional neural networks. ACM Transactions on Graphics 35, 1 (2015), 3:1–3:12. Google ScholarDigital Library
    15. R. Hadsell, S. Chopra, and Y. LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06). Google ScholarDigital Library
    16. X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg. 2015. MatchNet: Unifying feature and metric learning for patch-based matching. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15).Google Scholar
    17. Q.-X. Huang, H. Su, and L. Guibas. 2013. Fine-grained semi-supervised labeling of large shape collections. ACM Transactions on Graphics 32, 6 (2013), 190:1–190:10. Google ScholarDigital Library
    18. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. CoRR.Google ScholarDigital Library
    19. A. E. Johnson and M. Hebert. 1999. Using spin images for efficient object recognition in cluttered 3D scenes. In IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 5 (1999), 433–449. Google ScholarDigital Library
    20. E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri. 2017. 3D shape segmentation with projective convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15).Google Scholar
    21. E. Kalogerakis, A. Hertzmann, and K. Singh. 2010. Learning 3D mesh segmentation and labeling. ACM Transactions on Graphics 29, 4 (2010), 102:1–102:12. Google ScholarDigital Library
    22. M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz. 2004. Symmetry descriptors and 3D shape matching. In Proceedings of the Symposium on Geometry Processing (SGP’04). Google ScholarDigital Library
    23. V. G. Kim, S. Chaudhuri, L. Guibas, and T. Funkhouser. 2014. Shape2Pose: Human-centric shape analysis. ACM Transactions on Graphics 33, 4 (2014), 120:1–120:12. Google ScholarDigital Library
    24. V. G. Kim, W. Li, N. J. Mitra, S. Chaudhuri, S. DiVerdi, and T. Funkhouser. 2013. Learning part-based templates from large collections of 3D shapes. ACM Transactions on Graphics 32, 4 (2013), 70:1–70:12. Google ScholarDigital Library
    25. D. P. Kingma and J. Ba. 2014. Adam: A method for stochastic optimization. CoRR.Google Scholar
    26. A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. The Conference and Workshop on Neural Information Processing Systems (NIPS’12). Google ScholarDigital Library
    27. K. Lai, L. Bo, and D. Fox. 2014. Unsupervised feature learning for 3D scene labeling. In IEEE International Conference on Robotics and Automation (ICRA’14).Google Scholar
    28. G. Lavoue. 2012. Combination of bag-of-words descriptors for robust partial shape retrieval. The Visual Computer 28, 9 (2012), 931–942. Google ScholarDigital Library
    29. R. Litman, A. Bronstein, M. Bronstein, and U. Castellani. 2014. Supervised learning of bag-of-features shape descriptors using sparse coding. Computer Graphics Forum 33, 5 (2014), 127–136.Google ScholarDigital Library
    30. Y. Liu, H. Zha, and H. Qin. 2006. Shape topics: A compact representation and new algorithms for 3D partial shape retrieval. IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06). Google ScholarDigital Library
    31. J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst. 2015. Geodesic convolutional neural networks on Riemannian manifolds. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 37–45. Google ScholarDigital Library
    32. D. Maturana and S. Scherer. 2015. 3D convolutional neural networks for landing zone detection from LiDAR. In IEEE International Conference on Robotics and Automation (ICRA’15).Google Scholar
    33. F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein. 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17).Google Scholar
    34. M. Novotni and R. Klein. 2003. 3D Zernike descriptors for content based shape retrieval. The 8th ACM Symposium on Solid Modeling and Applications. Google ScholarDigital Library
    35. R. Ohbuchi and T. Furuya. 2010. Distance metric learning and feature combination for shape-based 3D model retrieval. Proc. 3DOR. Google ScholarDigital Library
    36. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. 2002. Shape distributions. ACM Transactions on Graphics 21, 4 (2002), 807–832. Google ScholarDigital Library
    37. M. Ovsjanikov, W. Li, L. Guibas, and N. J. Mitra. 2011. Exploration of continuous variability in collections of 3D shapes. ACM Transactions on Graphics 30, 4 (2011), 33:1–33:10. Google ScholarDigital Library
    38. C. R. Qi, H. Su, K. Mo, and L. J. Guibas. 2017. PointNet: Deep learning on point sets for 3D classification and segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17).Google Scholar
    39. C. R. Qi, H. Su, M. Niener, A. Dai, M. Yan, and L. J. Guibas. 2016. Volumetric and multi-view CNNs for object classification on 3D data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 5648–5656.Google Scholar
    40. E. Rodola, S. Bulo, T. Windheuser, M. Vestner, and D. Cremers. 2014. Dense non-rigid shape correspondence using random forests. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14). Google ScholarDigital Library
    41. E. Rodola, L. Cosmo, O. Litany, M. M. Bronstein, A. M. Bronstein, N. Audebert, A. Ben Hamza, A. Boulch, U. Castellani, M. N. Do, A.-D. Duong, T. Furuya, A. Gasparetto, Y. Hong, J. Kim, B. Le Saux, R. Litman, M. Masoumi, G. Minello, H.-D. Nguyen, V.-T. Nguyen, R. Ohbuchi, V.-K. Pham, T. V. Phan, M. Rezaei, A. Torsello, M.-T. Tran, Q.-T. Tran, B. Truong, L. Wan, and C. Zou. 2017. Deformable shape retrieval with missing parts. In Eurographics Workshop on 3D Object Retrieval (3DOR’17).Google Scholar
    42. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115, 3 (2015), 211–252. Google ScholarDigital Library
    43. D. Saupe and D. V. Vranic. 2001. 3D model retrieval with spherical harmonics and moments. In Symposium on Pattern Recognition. 392–397. Google ScholarDigital Library
    44. M. Savva, F. Yu, Hao Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, Hang Su, S. Bai, X. Bai, N. Fish, J. Han, E. Kalogerakis, E. G. Learned-Miller, Y. Li, M. Liao, S. Maji, A. Tatsuma, Y. Wang, N. Zhang, and Z. Zhou. 2016. Large-scale 3D shape retrieval from shapenet core55. Eurographics Workshop on 3D Object Retrieval (3DOR’16). Google ScholarDigital Library
    45. L. Shapira, S. Shalom, A. Shamir, D. Cohen-Or, and H. Zhang. 2010. Contextual part analogies in 3D objects. International Journal of Computer Vision 89, 2–3 (2010), 309–326. Google ScholarDigital Library
    46. E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer. 2015. Discriminative learning of deep convolutional feature point descriptors. In IEEE International Conference on Computer Vision (ICCV’15). 9. Google ScholarDigital Library
    47. K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. CoRR.Google Scholar
    48. A. Sinha, J. Bai, and K. Ramani. 2016. Deep learning 3D shape surfaces using geometry images. European Conference on Computer Vision (ECCV’16).Google Scholar
    49. R. Socher, B. Huval, B. Bhat, C. D. Manning, and A. Y. Ng. 2012. Convolutional-recursive deep learning for 3D object classification. The Conference and Workshop on Neural Information Processing Systems (NIPS’12). 656–664. Google ScholarDigital Library
    50. S. Song and J. Xiao. 2016. Deep sliding shapes for amodal 3d object detection in RGB-D images. European Conference on Computer Vision (ECCV’16).Google Scholar
    51. O. Sorkine and M. Alexa. 2007. As-rigid-as-possible surface modeling. In Proceedings of the Symposium on Geometry Processing (SGP’07). Google ScholarDigital Library
    52. H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller. 2015. Multi-view convolutional neural networks for 3D shape recognition. In Proceedings of ICCV. Google ScholarDigital Library
    53. R. W. Sumner, J. Schmid, and M. Pauly. 2007. Embedded deformation for shape manipulation. ACM Trans. Graph. 26, 3 (2007). Google ScholarDigital Library
    54. F. Tombari, S. Salti, and L. Di Stefano. 2010. Unique signatures of histograms for local surface description. European Conference on Computer Vision (ECCV’10). Google ScholarDigital Library
    55. L. Wei, Q. Huang, D. Ceylan, E. Vouga, and H. Li. 2016. Dense human body correspondences using convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16).Google Scholar
    56. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 2015. 3D shapenets: A deep representation for volumetric shapes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 1912–1920.Google Scholar
    57. Y. Xian, B. Schiele, and Z. Akata. 2017. Zero-shot learning – The good, the bad and the ugly. CoRR (2017).Google Scholar
    58. J. Xie, Y. Fang, F. Zhu, and E. Wong. 2015. Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15).Google Scholar
    59. K. Xu, V. G. Kim, Q. Huang, N. Mitra, and E. Kalogerakis. 2016. Data-driven shape analysis and processing. In SIGGRAPH ASIA 2016 Courses (SA’16). ACM. Google ScholarDigital Library
    60. K. M. Yi, E. Trulls, V. Lepetit, and P. Fua. 2016. LIFT: Learned invariant feature transform. European Conference on Computer Vision (ECCV’16).Google Scholar
    61. L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas. 2016. A scalable active framework for region annotation in 3D shape collections. ACM Transactions on Graphics 35, 6 (2016), 210:1–210:12. Google ScholarDigital Library
    62. L. Yi, H. Su, X. Guo, and L. Guibas. 2017. Synchronized spectral CNN for 3D shape segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17).Google Scholar
    63. A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser. 2016. 3DMatch: Learning local geometric descriptors from RGB-D reconstructions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17).Google Scholar
    64. E. Zhang, K. Mischaikow, and G. Turk. 2005. Feature-based surface parameterization and texture mapping. ACM Transactions on Graphics 24, 1 (2005), 1–27. Google ScholarDigital Library

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