Efficient Registration of High-Resolution Feature Enhanced Point Clouds

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

[1]  Reiner M. Dreizler,et al.  Theoretical Mechanics: Theoretical Physics 1 , 2010 .

[2]  Gary K. L. Tam,et al.  Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid , 2013, IEEE Transactions on Visualization and Computer Graphics.

[3]  Joaquim Salvi,et al.  A Qualitative Review on 3D Coarse Registration Methods , 2015, ACM Comput. Surv..

[4]  H. Hong,et al.  Colored Point Cloud Registration with Improved Hue-Assisted Normal Distributions Transform , 2022 .

[5]  Michael Korn,et al.  Color supported generalized-ICP , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[6]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[7]  Sing Bing Kang,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[8]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[9]  Didier Stricker,et al.  Gravitational Approach for Point Set Registration , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Maarten Weyn,et al.  A Survey of Rigid 3D Pointcloud Registration Algorithms , 2014 .

[11]  Hao Men,et al.  Hue-assisted automatic registration of color point clouds , 2014, J. Comput. Des. Eng..

[12]  Wolfgang Straßer,et al.  Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform , 2008, 2008 IEEE International Conference on Robotics and Automation.

[13]  Floris Ernst,et al.  Enhanced Optical Head Tracking for Cranial Radiation Therapy: Supporting Surface Registration by Cutaneous Structures. , 2016, International journal of radiation oncology, biology, physics.

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

[15]  Takeo Kanade,et al.  A Correlation-Based Approach to Robust Point Set Registration , 2004, ECCV.

[16]  Andreas Birk,et al.  Beyond points: Evaluating recent 3D scan-matching algorithms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Eric Mjolsness,et al.  New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence , 1998, NIPS.

[18]  H. Chui,et al.  A feature registration framework using mixture models , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[19]  S. Druon,et al.  Color Constrained ICP for Registration of Large Unstructured 3D Color Data Sets , 2006, 2006 IEEE International Conference on Information Acquisition.

[20]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[21]  Hans Martin Kjer,et al.  Evaluation of surface registration algorithms for PET motion correction , 2010 .

[22]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[23]  Yonghuai Liu,et al.  3D Imaging, Analysis and Applications , 2012, Springer London.

[24]  Adrian Hilton,et al.  Influence of Colour and Feature Geometry on Multi-modal 3D Point Clouds Data Registration , 2014, 2014 2nd International Conference on 3D Vision.

[25]  James S. Duncan,et al.  A Robust Point Matching Algorithm for Autoradiograph Alignment , 1996, VBC.

[26]  Shi-Min Hu,et al.  Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes , 2006, International Journal of Computer Vision.

[27]  Roland Siegwart,et al.  A Review of Point Cloud Registration Algorithms for Mobile Robotics , 2015, Found. Trends Robotics.

[28]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[29]  Myung Jin Chung,et al.  3D environment reconstruction using modified color ICP algorithm by fusion of a camera and a 3D laser range finder , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Roland Siegwart,et al.  Comparing ICP variants on real-world data sets , 2013, Auton. Robots.

[31]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D‐NDT , 2007, J. Field Robotics.

[32]  Soon-Yong Park,et al.  An accurate and fast point-to-plane registration technique , 2003, Pattern Recognit. Lett..

[33]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[34]  W. Nolting.  Grundkurs theoretische Physik , 2002 .

[35]  Robert Bergevin,et al.  Towards a General Multi-View Registration Technique , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Mohamed Amine Kamoun,et al.  On Performance Evaluation of Registration Algorithms for 3D Point Clouds , 2016, 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV).

[37]  Hao Men,et al.  Color point cloud registration with 4D ICP algorithm , 2011, 2011 IEEE International Conference on Robotics and Automation.

[38]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Miguel Á. Carreira-Perpiñán,et al.  Non-rigid point set registration: Coherent Point Drift , 2006, NIPS.

[40]  R. Berkvens,et al.  A Benchmark Survey of Rigid 3D Point Cloud Registration Algorithms , 2015 .

[41]  Oscar Cordón,et al.  A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling , 2011, Comput. Vis. Image Underst..

[42]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[44]  André Crosnier,et al.  Pair-wise Registration of 3D/Color Data Sets with ICP , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.