Abstract
The soaring popularity of deep learning in a wide variety of fields ranging from computer vision and speech recognition to self-driving vehicles has sparked a flurry of research interest from both academia and industry. In this paper, we propose a deep learning approach to 3D shape retrieval using a multi-level feature learning paradigm. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Then, mid-level features are generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-of-feature descriptors. Finally, high-level shape features are learned by applying a deep auto-encoder on mid-level features. Extensive experiments on SHREC-2014 and SHREC-2015 datasets demonstrate the much better performance of the proposed framework in comparison with state-of-the-art methods.
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Rustamov R (2007) Laplace-Beltrami eigenfunctions for deformation invariant shape representation. In: Proc. Symp. Geometry Processing, pp 225–233
Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multi-scale signature based on heat diffusion. Comput Graphics Forum 28(5):1383–1392
Aubry M, Schlickewei U, Cremers D (2011) The wave kernel signature: A quantum mechanical approach to shape analysis. In: Proc. Computational Methods for the Innovative Design of Electrical Devices, pp 1626–1633
Bronstein M, Kokkinos I (2010) Scale-invariant heat kernel signatures for non-rigid shape recognition. In: Proceedings of the CVPR, pp 1704–1711
Li C, Ben Hamza A (2013) A multiresolution descriptor for deformable 3D shape retrieval. Vis Comput 29:513–524
Chaudhari A, Leahy R, Wise B, Lane N, Badawi R, Joshi A (2014) Global point signature for shape analysis of carpal bones. Phys Med Biol 59:961–973
Ye J, Yu Y (2015) A fast modal space transform for robust nonrigid shape retrieval. Vis Comput 32 (5):553–568
Mohamed W, Ben Hamza A (2016) Deformable 3D shape retrieval using a spectral geometric descriptor. Appl Intell 45(2):2213–229
Pickup D, Sun X, Rosin P, Martin R, Cheng Z, Lian Z, Aono M, Ben Hamza A, Bronstein A, Bronstein M, Bu S, Castellani U, Cheng S, Garro V, Giachetti V, Godil A, Han J, Johan H, Lai L, Li L, Li C, Li H, Litman R, Liu X, Liu Z, Lu Y, Tatsuma A, Ye J (2014) SHREC, 14 track: Shape retrieval of non-rigid 3D human models. In: Proc. Eurographics Workshop on 3D Object Retrieval, pp 1–10
Lian JZZ, Choi S, ElNaghy H, El-Sana J, Furuya T, Giachetti A, Isaia RGL, Lai L, Li C, Li H, Limberger F, Martin R, Nakanishi R, Nonato ANL, Ohbuchi R, Pevzner K, Pickup D, Rosin P, Sharf A, Sun L, Sun X, Tari S, Unal G, Wilson R (2015) SHREC’15 track: Non-rigid 3D shape retrieval. In: Proc. Eurographics Workshop on 3D, Object Retrieval, pp 1–14
Reuter M, Wolter F, Peinecke N (2006) Laplace-Beltrami spectra as Shape-DNA’ of surfaces and solids. Comput Aided Des 38(4):342–366
Bronstein A, Bronstein M, Guibas L, Ovsjanikov M (2011) Shape Google: Geometric words and expressions for invariant shape retrieval. ACM Trans Graphics 1:30
Litman R, Bronstein A, Bronstein M, Castellani U (2014) Supervised learning of bag-of-features shape descriptors using sparse coding. Comput Graphics Forum 33(5):127–136
Noda K, Yamaguchi Y, Nakadai K, Okuno H, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737
Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D ShapeNets: A deep representation for volumetric shapes. In: Proc. CVPR, pp 1912–1920
Savva M, Yu F, Su H, aono M, Chen B, Cohen-Or D, Deng W, Su H, Bai S, Bai X, Fish JHN, Kalogerakis E, Learned-Miller E, Li Y, Liao M, Maji S, Wang Y, Zhang N, Zhou Z (2016) SHREC’16 track: Large-scale 3D shape retrieval from ShapeNet Core55. In: Proc. Eurographics Workshop on 3D Object Retrieval
Qi C, Su H, Nießner M., Dai A, Yan M, Guibas L (2016) Volumetric and multi-view CNNs for object classification on 3D data. In: Proc. CVPR
Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3D shape retrieval Neurocomputing
Fang Y, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong E (2015) 3D deep shape descriptor. In: Proc. CVPR, pp 2319–2328
Bu S, Liu Z, Han J, Wu J, Ji R (2014) Learning high-level feature by deep belief networks for 3-D model retrieval and recognition. IEEE Trans Multimedia 24(16):2154–2167
Lipman Y, Rustamov R, Funkhouser T (2010) Biharmonic distance. ACM Trans Graphics 29(3):1–11
Li C, Ben Hamza A (2013) Intrinsic spatial pyramid matching for deformable 3D shape retrieval. Int Journal Multimedia Information Retrieval 2:261–271
Dong W, Li X, Zhang D, Shi G (2010) Sparsity-basedx image denoising via dictionary learning and structural clustering. In: Proc. CVPR, pp 3360–3367
Ben Hamza A, Krim H (2006) Geodesic matching of triangulated surfaces. IEEE Trans Image Processing 15(8):2249–2258
Lian Z, Godil A, Bustos B, Daoudi M, Hermans J, Kawamura S, Kurita Y, Lavoué G., Nguyen H, Ohbuchi R, Ohkita Y, Ohishi Y, Porikli F, Reuter M, Sipiran I, Smeets D, Suetens P, Tabia H, Vandermeulen D (2011) SHREC’11 track: Shape retrieval on non-rigid 3D watertight meshes. In: Proc. Eurographics/ACM SIGGRAPH Symp 3D. Object Retrieval, pp 79–88
Giachetti A, Lovato C (2012) Radial symmetry detection and shape characterization with the multiscale area projection transform. Comput Graphics Forum 31(5):1669–1678
Pickup D, Sun X, Rosin P, Martin R (2015) Geometry and context for semantic correspondences and functionality recognition in manmade 3D shapes. Pattern Recogn 48(8):2500–2512
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605
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Ghodrati, H., Hamza, A.B. Nonrigid 3D shape retrieval using deep auto-encoders. Appl Intell 47, 44–61 (2017). https://doi.org/10.1007/s10489-016-0880-1
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DOI: https://doi.org/10.1007/s10489-016-0880-1