Multiview registration-based handheld 3D profiling system using visual navigation and structured light

References

• Salvi, J. A state of the art in structured light patterns for surface profilometry. Pattern Recognit. 2010, 43, 2666–2680.
   Web of Science ®Google Scholar
• Li, Y.; Gu, P. Free-form surface inspection techniques state of the art review. Comput. Aided Des. 2004, 36, 1395–1417.
   Web of Science ®Google Scholar
• Bruno, F. Experimentation of structured light and stereo vision for underwater 3-D reconstruction. ISPRS J. Photogram. Remote Sens. 2011, 66, 508–518.
   Web of Science ®Google Scholar
• Shi, C.-Q.; Zhang, L.-Y. A 3-D shape measurement system based on random pattern projection. In 2010 Fifth International Conference on Frontier of Computer Science and Technology (FCST), Changchun, Jilin Province, China, August 18–22, 2010, IEEE Computer Society, Los Alamitos, CA, 2010.
   Google Scholar
• Kim, M.Y.; Ayaz, S.M.; Park, J.; Roh, Y. Adaptive 3-D sensing system based on variable magnification using stereo vision and structured light. Opt. Lasers Eng. 2014, 55, 113–127.
   Web of Science ®Google Scholar
• Geng, J. Structured-light 3-D surface imaging: A tutorial. Adv. Opt. Photon. 2011, 3, 128–160.
   Web of Science ®Google Scholar
• Strobl, K.H. The self-referenced DLR 3-D-modeler. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009 (IROS 2009), Hyatt Regency St. Louis Riverfront, St. Louis, MO, USA, October 10–15, 2009; IEEE: Washington, DC, USA, 2009.
   Google Scholar
• Munkelt, C. Hand-held 3-D scanning with automatic multi-view registration based on optical and inertial pose estimation. In Wolfgang Osten, Fringe 2013; Springer: Berlin, Heidelberg, 2014; pp. 809–814.
   Google Scholar
• Kleiner, B. Hand-held 3-D scanning with automatic multi-view registration based on visual-inertial navigation. Int. J. Optomechatron. 2014, 8, 313–325.
   Web of Science ®Google Scholar
• Byczkowski, T.; Lang, J. A stereo-based system with inertial navigation for outdoor 3-D scanning. In Canadian Conference on Computer and Robot Vision, 2009 (CRV′09), Kelowna, British Columbia, Canada, May 25–27, 2009; IEEE: Washington, DC, USA, 2009.
   Google Scholar
• Zhang, Z. Microsoft kinect sensor and its effect. IEEE MultiMedia 2012, 19, 4–10.
   Web of Science ®Google Scholar
• Newcombe, R.A. KinectFusion: Real-time dense surface mapping and tracking. In 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland, October 26–29, 2011, IEEE: Washington, DC, USA, 2011.
   Google Scholar
• Izadi, S. KinectFusion: Real-time 3-D reconstruction and interaction using a moving depth camera. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, Santa Barbara, CA, USA, October 16–19, 2011; ACM: New York, NY, USA, 2011.
   Google Scholar
• Han, J. Enhanced computer vision with microsoft kinect sensor: A review. IEEE Trans. Cybernet. 2013, 43, 1318–1334.
   PubMed Web of Science ®Google Scholar
• Schaffer, M.; Grosse, M.; Kowarschik, R. High-speed pattern projection for three-dimensional shape measurement using laser speckles. Appl. Opt. 2010, 49, 3622–3629.
   PubMed Web of Science ®Google Scholar
• An, D.; Woodward, A.; Delmas, P.; Gimel’farb, G.; Morris, J.; Marquez, J. Comparison of active structure lighting mono and stereo camera systems: Application to 3-D face acquisition. In Seventh Mexican International Conference on Computer Science 2006 (ENC′06), San Luis Potosi, Mexico, September 18–22, 2006; IEEE Computer Society: Washington, DC, USA, 2006.
   Google Scholar
• Hu, E.; He, Y. Surface profile measurement of moving objects by using an improved π phase-shifting Fourier transform profilometry. Opt. Lasers Eng. 2009, 47, 57–61.
   Web of Science ®Google Scholar
• Chen, C.-S.; Hung, Y.-P.; Chiang, C.-C.; Wu, J.-L. Range data acquisition using color structured lighting, & stereo vision. Image Vis. Comput. 1997, 15, 445–456.
   Web of Science ®Google Scholar
• Hébert, P. A self-referenced hand-held range sensor. In Proceedings of Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, Canada, 28 May–1 June, 2001; IEEE Computer Society: Los Alamitos, CA, 2001.
   Google Scholar
• Khoury, R. An enhanced positioning algorithm for a self-referencing hand-held 3-D sensor. In The 3rd Canadian Conference on Computer and Robot Vision, Quebec City, Canada, June 7–9, 2006; IEEE Computer Society: Los Alamitos, CA, 2006.
   Google Scholar
• Weiss, S.; Siegwart, R. Real-time metric state estimation for modular vision-inertial systems. In 2011 IEEE International Conference on Robotics and Automation (ICRA), International Conference Center, Shanghai, China, May 9–13, 2011; IEEE: Washington, DC, 2011, pp. 4531–4537.
   Google Scholar
• Leutenegger, S.; Furgale, P.; Rabaud, V.; Chli, M.; Konolige, K.; Siegwart, R. Keyframe based visual-inertial SLAM using nonlinear optimization. In Proceedings of Robotics: Science and Systems, Berlin, Germany, June 24–28, RSS Foundation: National University of Singapore, Republic of Singapore, 2013 (online proceedings).
   Google Scholar
• Li, M.; Mourikis, A.I. High-precision, consistent EKF-based visual–inertial odometry. Int. J. Robot. Res. 2013, 32, 690–711.
   Web of Science ®Google Scholar
• Strobl, K.H.; Mair, E.; Hirzinger, G. Image-based pose estimation for 3-D modeling in rapid, hand-held motion. In 2011 IEEE International Conference on Robotics and Automation (ICRA), International Conference Center, Shanghai, China, May 9–13, 2011; IEEE: Washington, DC, 2011.
   Google Scholar
• Ayaz, S.M.; Danish, K.; Bang, J.Y.; Park, S.I.; Roh, Y.; Kim, M.Y. A multi-view stereo based 3-D hand-held scanning system using visual-inertial navigation and structured light. In MATEC Web of Conferences, Neuchatel, Switzerland, October 14–16, 2015; EDP Sciences Publisher: France, 2015; 32, pp. 06004.
   Google Scholar
• Bay, H. Speeded-up robust features (SURF). Comput. Vis. Image Understand. 2008, 110, 346–359.
   Web of Science ®Google Scholar
• Bradski, G.; Kaehler, A. Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Inc.: California, USA, 2008.
   Google Scholar
• Fischler, M.A.; Bolles, R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 1981, 24, 381–395.
   Web of Science ®Google Scholar
• Zhang, Z.A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intelligence 2000, 22, 1330–1334.
   Web of Science ®Google Scholar
• Wijenayake, U.; Park, S.-Y. Dual pseudorandom array technique for error correction and hole filling of color structured-light three-dimensional scanning. Opt. Eng. 2015, 54, 043109–043109.
   Web of Science ®Google Scholar
• Konolige, K. Small vision systems: Hardware and implementation. Robot. Res. 1998, 8 (Robotics Research, The Eighth International Symposium), 203–212 (Springer: London).
   Google Scholar
• Lim, J.S. Two-dimensional Signal and Image Processing. Prentice Hall: Englewood Cliffs, NJ, 1990, 710 p.
   Google Scholar
• Mian, A.S.; Bennamoun, M.; Owens, R. Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. Pattern Anal. Mach. Intelligence 2006, 28, 1584–1601.
   PubMed Web of Science ®Google Scholar
• Chen, Y.; Medioni, G. Object modelling by registration of multiple range images. Image Vis. Comput. 1992, 10, 145–155 (Butterworth-Heinemann).
   Web of Science ®Google Scholar
• Besl, P.J.; McKay, N.D. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intelligence 1992, 14, 239–256 (IEEE Computer Society: Los Alamitos, CA).
   Web of Science ®Google Scholar
• Girardeau-Montaut, D. Cloud Compare: 3D point cloud and mesh processing software, open-source project. On-line: http://www.danielgm.net/cc, Accessed 2016, 4 (03).
   Google Scholar