Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.

[1]  Hamid Jafarkhani,et al.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..

[2]  Lihui Wang,et al.  Review: Advances in 3D data acquisition and processing for industrial applications , 2010 .

[3]  Sébastien Ourselin,et al.  A Survey of Methods for 3D Histology Reconstruction , 2018, Medical Image Anal..

[4]  Christopher J. Taylor,et al.  Statistical models of shape - optimisation and evaluation , 2008 .

[5]  Jiansheng Peng,et al.  Single image 3D object reconstruction based on deep learning: A review , 2020, Multimedia Tools and Applications.

[6]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[7]  Matthias Nießner,et al.  State of the Art on 3D Reconstruction with RGB‐D Cameras , 2018, Comput. Graph. Forum.

[8]  Song Bai,et al.  Triplet-Center Loss for Multi-view 3D Object Retrieval , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[9]  Yeung Sam Hung,et al.  3D Model Reconstruction from Turntable Sequence with Multiple -View Triangulation , 2009, ISVC.

[10]  Reinhard Klein,et al.  A geometric approach to 3D object comparison , 2001, Proceedings International Conference on Shape Modeling and Applications.

[11]  BENJAMIN BUSTOS,et al.  Feature-based similarity search in 3D object databases , 2005, CSUR.

[12]  K. Cook.  An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection , 2017 .

[13]  Fernando Carvajal-Ramírez,et al.  Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Adrien Bartoli,et al.  3D Shape Registration , 2012, 3D Imaging, Analysis and Applications.

[15]  Sven J. Dickinson,et al.  Skeleton based shape matching and retrieval , 2003, 2003 Shape Modeling International..

[16]  Michael J. Black,et al.  3D Menagerie: Modeling the 3D Shape and Pose of Animals , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Zhong Liu,et al.  An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors , 2017, Sensors.

[18]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[19]  Xiang Zhou,et al.  A flexible 3D laser scanning system using a robotic arm , 2017, Optical Metrology.

[20]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[21]  Mohammed Bennamoun,et al.  On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes , 2009, International Journal of Computer Vision.

[22]  D. Lague,et al.  Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) , 2013, 1302.1183.

[23]  Maxime Sermesant,et al.  How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function , 2017, The Journal of thoracic and cardiovascular surgery.

[24]  Alberto J. Pérez Jiménez,et al.  A System for In-Line 3D Inspection without Hidden Surfaces , 2018, Sensors.

[25]  Edward K. Wong,et al.  DeepShape: Deep-Learned Shape Descriptor for 3D Shape Retrieval , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  A. Bartoli,et al.  3 D Shape Registration , 2011 .

[27]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Stefan Zachow,et al.  Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative , 2019, Medical Image Anal..

[29]  Stefanos Zafeiriou,et al.  Large Scale 3D Morphable Models , 2017, International Journal of Computer Vision.

[30]  V. Frémont,et al.  Turntable-based 3D object reconstruction , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[31]  Jorge Santolaria,et al.  3D Geometrical Inspection of Complex Geometry Parts Using a Novel Laser Triangulation Sensor and a Robot , 2011, Sensors.

[32]  Amit Kumar Singh,et al.  Framework for Automated GD&T Inspection Using 3D Scanner , 2018 .

[33]  Kok-Lim Low.  Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration , 2004 .

[34]  Cezary Specht,et al.  Using UAV Photogrammetry to Analyse Changes in the Coastal Zone Based on the Sopot Tombolo (Salient) Measurement Project , 2020, Sensors.

[35]  Christos Davatzikos,et al.  Individualized statistical learning from medical image databases: Application to identification of brain lesions , 2014, Medical Image Anal..

[36]  K. Mardia,et al.  The statistical analysis of shape data , 1989 .

[37]  Andrea Dall'Asta,et al.  Quantitative analysis of fetal facial morphology using 3D ultrasound and statistical shape modeling: a feasibility study , 2017, American journal of obstetrics and gynecology.

[38]  Vincent Barra,et al.  3D shape retrieval using Kernels on Extended Reeb Graphs , 2013, Pattern Recognit..

[39]  Levente Hajder,et al.  High-quality structured-light scanning of 3D objects using turntable , 2012, 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom).