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An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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An integrated framework for 3 d modeling, object detection, and pose estimation from point-clouds

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com AN INTEGRATED FRAMEWORK FOR 3-D MODELING, OBJECT DETECTION, AND POSE ESTIMATION FROM POINT-CLOUDS By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: 3-D modeling, object detection, and pose estimation are three of the most challenging tasks in the area of 3-D computer vision. This paper presents a novel algorithm to perform these tasks simultaneously from unordered point-clouds. Given a set of input point-clouds in the presence of clutter and occlusion, an initial model is first constructed by performing pair-wise registration between any two point-clouds. The resulting model is then updated from the remaining point- clouds using a novel model growing technique. Once the final model is reconstructed, the instances of the object are detected and the poses of its instances in the scenes are estimated. This algorithm is automatic, model free, and does not rely on any prior information about the objects in the scene. The algorithm was comprehensively tested on the University of Western Australia data set. Experimental results show that our algorithm achieved accurate modeling, detection, and pose estimation performance
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: With the rapid development of 3-D surface measurement techniques point-clouds are now readily available and popular. The availability of low-cost point-clouds and powerful computing devices is inspiring ample research in several areas including measurement, computer vision, and computer graphics. Among these, 3-D modeling and 3-D object recognition are two of the major fundamental problems. The task of 3-D modeling is to align the point-clouds which are measured at different viewpoints, and merge these point-clouds to obtain a complete model of an object Meanwhile, the aim of 3-D object recognition is to correctly estimate the identities and poses (locations and orientations) of these objects in a scene. 3-D object modeling, detection, and recognition have a number of applications including scene measurement, autonomous mapping, city planning, reverse engineering, remote sensing, industrial inspection, and biometrics . Most existing 3-D object recognition algorithms follow a model-based paradigm.During the offline preprocessing phase, 3-D models of objects of interest are first constructed and stored in a library along with a set of suitably extracted features. During the online recognition phase, features are extracted from a point-cloud of the scene and matched against these model features to recognize potential objects Several features have been proposed to enhance the performance of 3- D object recognition, including point signatures,spin image,3-D tensor,exponential map rotational projection statistics (RoPS), signature of histograms of orientations (SHOT) and tri-spin image (TriSI) However, these 3-D object recognition algorithms require prior 3-D models of objects. They are therefore, unable to recognize unknown objects in a scene. To perform 3-D object modeling/recognition, multiple point-clouds must be registered in a common coordinate basis . A complete registration process usually consists of two steps: coarse and fine registrations. Coarse registration can be performed by either manual alignment, motion tracking, or local feature matching . Local feature matching-based algorithms automatically extract corresponding points from any two (pairwise registration) or multiple point-clouds and coarsely register them by minimizing the distance between these points. Due to the nature that they are automatic, flexible, and cheap, local feature matchingbased algorithms have been intensively studied. Once these point-clouds are coarsely registered, a fine registration algorithm is applied to iteratively refine the initial coarse registration. Examples of fine registration algorithms include the iterative closest point (ICP) algorithm, Chen and Medioni’s algorithm, and the signed distance
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com fields-based algorithm. These existing algorithms were proposed to perform matching either between point-clouds of isolated scenes (in the case of 3-D modeling), or between a cluttered scene and an isolated model (in the case of 3-D object recognition). To the best of our knowledge, there is limited research in the literature that covers the matching between two cluttered scenes for the modeling of isolated 3-D objects (rather than 3-D scenes). For more details on 3-D object recognition algorithms, the reader is referred to a comprehensive and contemporary survey. This paper is motivated by this research niche to detect unknown objects without any prior information, and to model these objects from a set of cluttered scenes. In this paper, 3-D object detection and modeling is performed based on the observation that, an object may appear in different scenes due to the movement of the object or sensor. It is therefore, possible to detect, segment, and reconstruct objects that appear multiple times in a set of point-clouds. A system with such capability has several applications. For example, a robot with a 3-D sensor can automatically detect unknown objects and hand them in for labeling, to reduce the labor-consuming object labeling work. It can also acquire a data set of 3-D models of objects in a room by roaming around, without isolately placing each object in a controlled environment (e.g., a turntable with a clear background). Moreover, the surge of low-cost 3-D scanners with an increasingly higher resolution (e.g., the new Microsoft Kinect) will allow the use of the proposed framework with many practical applications. Due to the presence of clutter and occlusion, together with the inability to provide an exact definition of what constitute an object, it is very challenging to automatically detect unknown objects in point-clouds. This paper proposes an integrated framework for 3-D modeling, object detection, and pose estimation from point-clouds. It first registers two cluttered scenes which have an overlapping surface area to build an initial model. The model is then updated by iteratively registering the model with the remaining unchecked pointclouds, and finally reconstructed by confidence thresholding. Consequently, the objects corresponding to this model can be detected and segmented from these point-clouds at the same time. The contributions of this paper are threefold. First, it performs 3-D object detection from point-clouds without any prior information (e.g., models). Second, it constructs 3-D models of multiple unknown objects from a set of cluttered point-clouds. Third, it performs modeling, detection, and pose estimation of unknown 3- D objects simultaneously.
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com The rest of this paper is organized as follows. Section II describes an overview of the proposed algorithm. Section III introduces the model initialization technique. Section IV describes the model growing technique. Section V presents the modeling, detection, and pose estimation technique. Section VI presents the experimental results and analysis. Section VII gives an insightful discussion. Section VIII concludes this paper.
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: In this paper, we presented a novel algorithm to model and detect 3-D objects, and to simultaneously estimate their poses from point-clouds. The algorithm consists of three main modules: model initialization, model growing, and modeling, detection, and pose estimation. Model initialization is performed by surface registration between any two pointclouds. The highly descriptive RoPS features and the 1-point RANSAC algorithm are used to achieve surface registration. Model growing is then performed by surface registration between the model and the unchecked point-clouds. During the process of model growing, a model update technique and a confidence scoring strategy are proposed. Finally, a final model is constructed by confidence thresholding and outlier cleaning. Meanwhile, The points in a point-cloud which can be registered well with the final model are detected as an instance of the object, and the pose of the object instance is estimated. The algorithm does not rely on any prior information and is automatic. Extensive experiments were conducted on the popular UWA data set. The performance of the proposed algorithm was tested in terms of modeling accuracy, detection rate, and pose estimation accuracy. Experimental results show that our algorithm can detect objects with a high detection rate. It can also build models and estimate their poses very accurately. Moreover, the proposed algorithm was compared with the state-of-the-art (i.e., the SHOT and spin image-based) algorithms. Experimental results show that our algorithm achieves the best results.
  10. 10. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] F. Meriaudeau et al., “3-D scanning of nonopaque objects by means of imaging emitted structured infrared patterns,” IEEE Trans. Instrum. Meas., vol. 59, no. 11, pp. 2898–2906, Nov. 2010. [2] A. P. P. Jongenelen, D. G. Bailey, A. D. Payne, A. A. Dorrington, and D. A. Carnegie, “Analysis of errors in ToF range imaging with dualfrequency modulation,” IEEE Trans. Instrum. Meas., vol. 60, no. 5, pp. 1861–1868, May 2011. [3] Y. Guo, J. Wan, M. Lu, and W. Niu, “A parts-based method for articulated target recognition in laser radar data,” Opt., Int. J. Light Electron Opt., vol. 124, no. 17, pp. 2727–2733, 2013. [4] J. Wang, L. Xu, X. Li, and Z. Quan, “A proposal to compensate platform attitude deviation’s impact on laser point cloud from airborne LiDAR,” IEEE Trans. Instrum. Meas., vol. 62, no. 9, pp. 2549–2558, Sep. 2013. [5] Y. Lei, M. Bennamoun, M. Hayat, and Y. Guo, “An efficient 3D face recognition approach using local geometrical signatures,” Pattern Recognit., vol. 47, no. 2, pp. 509–524, 2014. [6]Y. Guo, M. Bennamoun, F. A. Sohel, J. Wan, and M. Lu, “3D free form object recognition using rotational projection statistics,” in Proc. IEEE 14th Workshop Appl. Comput. Vis., Jan. 2013, pp. 1–8. [7] J. Chen, X. Wu, M. Y. Wang, and X. Li, “3D shape modeling using a self-developed hand- held 3D laser scanner and an efficient HT-ICP point cloud registration algorithm,” Opt. Laser Technol., vol. 45, pp. 414–423, Feb. 2012. [8] Y. Guo, F. Sohel, M. Bennamoun, J. Wan, and M. Lu, “An accurate and robust range image registration algorithm for 3D object modeling,” IEEE Trans. Multimedia, vol. 16, no. 5, pp. 1377– 1390, Aug. 2014. [9] M. S. Hosseini, B. N. Araabi, and H. Soltanian-Zadeh, “Pigment melanin: Pattern for iris recognition,” IEEE Trans. Instrum. Meas., vol. 59, no. 4, pp. 792–804, Apr. 2010.

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