Articulated 3D model matching using multi-scale histograms of shape features for customized additive manufacturing

Abstract The additive manufacturing (AM) has met a big challenge on account of customized shapes, especially complex and articulated models, designed by traditional approaches. For realizing AM processing, the reuse, matching, and modifications of the existing 3D digitized models are prerequisite to the applications which also avoid designing from start. In this study, a 3D model matching framework for articulated models serves as a solution for the initial digital design and model processing steps of AM. A discriminative shape descriptor is proposed, which is based on a slice-based model representation and combined with the shape feature distribution in the histogram form. Also a multi-scale histogram approach, integrating an improved Earth Mover's Distance (iEMD), is developed for feature matching which overcomes noise and scale perturbations. The experiments show better performances (average up 7.92 %∼15.80 %) than the classical competitors according to the retrieval performances evaluated by five typical measurements on the McGill database.

[1]  Yaoyao Fiona Zhao,et al.  Lattice Structure Design and Optimization With Additive Manufacturing Constraints , 2018, IEEE Transactions on Automation Science and Engineering.

[2]  Allison M. Okamura,et al.  Design of 3-D Printed Concentric Tube Robots , 2016, IEEE Transactions on Robotics.

[3]  Xindong Wu,et al.  3-D Object Retrieval With Hausdorff Distance Learning , 2014, IEEE Transactions on Industrial Electronics.

[4]  Huan Wang,et al.  Scene image classification using locality-constrained linear coding based on histogram intersection , 2018, Multimedia Tools and Applications.

[5]  Laurent D. Cohen,et al.  Matching 2D and 3D articulated shapes using the eccentricity transform , 2011, Comput. Vis. Image Underst..

[6]  Michael Werman,et al.  Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[7]  Matthieu van der Heijden,et al.  Moving to additive manufacturing for spare parts supply , 2019, Comput. Ind..

[8]  Han Tong Loh,et al.  3D CAD model matching from 2D local invariant features , 2010, Comput. Ind..

[9]  Jau-Ling Shih,et al.  A 3D model retrieval approach using the interior and exterior 3D shape information , 2009, Multimedia Tools and Applications.

[10]  Ayoub Al-Hamadi,et al.  A Multiresolution Approach to Model-Based 3-D Surface Quality Inspection , 2016, IEEE Transactions on Industrial Informatics.

[11]  Min Liu,et al.  VIV: Using visible internal volume to compute junction-aware shape descriptor of 3D articulated models , 2016, Neurocomputing.

[12]  Wei Yang,et al.  Chi-Squared Distance Metric Learning for Histogram Data , 2015 .

[13]  Hyungki Kim,et al.  Shape distribution-based retrieval of 3D CAD models at different levels of detail , 2016, Multimedia Tools and Applications.

[14]  Daniel R. Eyers,et al.  Industrial Additive Manufacturing: A manufacturing systems perspective , 2017, Comput. Ind..

[15]  Takuya Funatomi,et al.  Non-rigid registration of serial section images by blending transforms for 3D reconstruction , 2019, Pattern Recognit..

[16]  Michael J. Black,et al.  Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape , 2012, ECCV.

[17]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[18]  William C. Regli,et al.  Using shape distributions to compare solid models , 2002, SMA '02.

[19]  Hossein Ebrahimnezhad,et al.  Non-rigid 3D object retrieval using directional graph representation of wave kernel signature , 2018, Multimedia Tools and Applications.

[21]  Natraj Iyer,et al.  An Engineering Shape Benchmark for 3D Models , 2005 .

[22]  Chih-Hsing Chu,et al.  Shape similarity measurement for 3D mechanical part using D2 shape distribution and negative feature decomposition , 2011, Comput. Ind..

[23]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[24]  Xin Lin,et al.  Three-Dimensional CAD Model Matching With Anisotropic Diffusion Maps , 2018, IEEE Transactions on Industrial Informatics.

[25]  Han Ding,et al.  Variance-Minimization Iterative Matching Method for Free-Form Surfaces—Part I: Theory and Method , 2019, IEEE Transactions on Automation Science and Engineering.

[26]  Shengyong Chen,et al.  Discriminative Histogram Intersection Metric Learning and Its Applications , 2017, Journal of Computer Science and Technology.

[27]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[28]  Shanmuganathan Raman,et al.  3DSymm: Robust and Accurate 3D Reflection Symmetry Detection , 2020, Pattern Recognit..

[29]  Wotao Yin,et al.  A Parallel Method for Earth Mover’s Distance , 2018, J. Sci. Comput..

[30]  M. Salmi Additive Manufacturing Processes in Medical Applications , 2021, Materials.

[31]  William Puech,et al.  Analysis of digitized 3D mesh curvature histograms for reverse engineering , 2017, Comput. Ind..

[32]  Li Hao,et al.  CAD assembly model retrieval based on multi-source semantics information and weighted bipartite graph , 2018, Comput. Ind..

[33]  Robert J. Wood,et al.  An integrated design and fabrication strategy for entirely soft, autonomous robots , 2016, Nature.

[34]  Ching-Yuen Chan,et al.  A novel 3D model retrieval approach using combined shape distribution , 2012, Multimedia Tools and Applications.

[35]  Daniel Cohen-Or,et al.  Consistent mesh partitioning and skeletonisation using the shape diameter function , 2008, The Visual Computer.

[36]  Jianfeng Yu,et al.  An assembly retrieval approach based on shape distributions and Earth Mover’s Distance , 2016 .

[37]  J. Paul Robinson,et al.  Quadratic form: A robust metric for quantitative comparison of flow cytometric histograms , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[38]  Min Liu,et al.  Computing the Inner Distances of Volumetric Models for Articulated Shape Description with a Visibility Graph , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.