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Automatic Reconstruction of Textured 3D Models of Textured 3Dmodels Automatic Reconstruction Dipl.-Ing

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Automatic Reconstruction of Textured 3D Models of Textured 3Dmodels Automatic Reconstruction Dipl.-Ing Dipl.-Ing. Benjamin Pitzer Automatic Reconstruction of Textured 3D Models Automatic Reconstruction of Textured 3D Models Automatic Reconstruction of Textured Benjamin Pitzer 020 Benjamin Pitzer Automatic Reconstruction of Textured 3D Models Schriftenreihe Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie (KIT) Band 020 Eine Übersicht über alle bisher in dieser Schriftenreihe erschienenen Bände finden Sie am Ende des Buchs. Automatic Reconstruction of Textured 3D Models by Benjamin Pitzer Dissertation, Karlsruher Institut für Technologie (KIT) Fakultät für Maschinenbau Tag der mündlichen Prüfung: 22. Februar 2011 Referenten: Prof. Dr.-Ing. C. Stiller, Adj. Prof. Dr.-Ing. M. Brünig Impressum Karlsruher Institut für Technologie (KIT) KIT Scientific Publishing Straße am Forum 2 D-76131 Karlsruhe KIT Scientific Publishing is a registered trademark of Karlsruhe Institute of Technology. Reprint using the book cover is not allowed. www.ksp.kit.edu This document – excluding the cover – is licensed under the Creative Commons Attribution-Share Alike 3.0 DE License (CC BY-SA 3.0 DE): http://creativecommons.org/licenses/by-sa/3.0/de/ The cover page is licensed under the Creative Commons Attribution-No Derivatives 3.0 DE License (CC BY-ND 3.0 DE): http://creativecommons.org/licenses/by-nd/3.0/de/ Print on Demand 2014 ISSN 1613-4214 ISBN 978-3-86644-805-6 DOI: 10.5445/KSP/1000025619 Automatic Reconstruction of Textured 3D Models Zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften von der Fakultät für Maschinenbau der Universität Karlsruhe (TH) genehmigte Dissertation von DIPL.-ING.BENJAMIN PITZER aus Menlo Park, CA Hauptreferent: Prof. Dr.-Ing. C. Stiller Korreferent: Adj. Prof. Dr.-Ing. M. Brünig Tag der mündlichen Prüfung: 22.02.2011 Abstract 3D reconstruction and visualization of environments is increasingly important and there is a wide range of application areas where 3D models are required. Recon- structing 3D models has therefore been a major research focus in academia and industry. For example, large scale efforts for the reconstruction of city models at a global scale are currently underway. A major limitation in those efforts is that creating realistic 3D models of environments is a tedious and time consum- ing task. In particular, two major issues persist which prevent a broader adoption of 3D modeling techniques: a lack of affordable 3D scanning devices that enable an easy acquisition of 3D data and algorithms capable of automatically process- ing this data into 3D models. We believe that autonomous technologies, which are capable of generating textured 3D models of real environments, will make the modeling process affordable and enable a wide variety of new applications. This thesis addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. The contributions are solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models. We first present a robotic data acquisition system which allows us to automatically scan large environments in a short amount of time. We also propose a calibration procedure for this system that determines the internal and external calibration which is necessary to transform data from one sensor into the coordinate system of another sensor. Next, we present solutions for the multi-view data registration problem, which is essentially the problem of aligning the data of multiple 3D scans into a common coordinate system. We propose a novel non- rigid registration method based on a probabilistic SLAM framework. This method incorporates spatial correlation models as map priors to guide the optimization. Scans are aligned by optimizing robot pose estimates as well as scan points. We show that this non-rigid registration significantly improves the alignment. Next, we address the problem of reconstructing a consistent 3D surface representation from the registered point clouds. We propose a volumetric surface reconstruction method based on a Poisson framework. In a second step, we improve the accuracy of this reconstruction by optimizing the mesh vertices to achieve a better approx- imation of the true surface. We demonstrate that this method is very suitable for the reconstruction of indoor environments. Finally, we present a solution to the reconstruction of texture maps from multiple scans. Our texture reconstruction approach partitions the surface into segments, unfolds each segment onto a plane, and reconstructs a texture map by blending multiple views into a single composite. This technique results in a very realistic reconstruction of the surface appearance and greatly enhances the visual impression by adding more realism. Contents 1 Introduction 1 1.1 Motivation . 3 1.2 Problem Statement . 5 1.3 Taxonomy of 3D Reconstruction Systems . 5 1.3.1 System Architecture . 5 1.3.2 Object Type . 7 1.3.3 Sensor Type . 7 1.3.4 Data Representation . 9 1.4 State of the Art / Related Work . 10 1.4.1 Automatic 3D Modeling Robots . 10 1.4.2 3D Mapping for Navigation . 11 1.4.3 3D Reconstruction from Multiple Images . 12 1.4.4 3D Reconstruction of Objects . 13 1.4.5 Commercial Products . 13 1.5 Goal and Structure . 14 2 Data Acquisition 17 2.1 Introduction . 17 2.2 Physical Setup . 18 2.3 System Calibration . 19 2.3.1 Internal Calibration . 20 2.3.2 Extrinsic Calibration . 23 2.4 Exploration . 25 2.5 Conclusion . 26 IV CONTENTS 3 Multi-View Registration 29 3.1 Introduction . 29 3.2 Pairwise Rigid-Registration . 31 3.2.1 The Iterative Closest Point Algorithm (ICP) . 31 3.2.2 Correspondence Search . 33 3.2.3 Error Metric . 34 3.2.4 Experiments . 36 3.2.5 Conclusion . 38 3.3 Global Registration . 40 3.3.1 Pose Graphs . 41 3.3.2 Optimal Pose Estimation . 44 3.3.3 Experiments . 48 3.3.4 Conclusion . 50 3.4 Probabilistic Non-Rigid Registration . 52 3.4.1 Formulation of the Probabilistic SLAM Problem . 54 3.4.2 SLAM with Map Priors . 57 3.4.3 Probabilistic Motion Model . 59 3.4.4 Probabilistic Observation Model . 60 3.4.5 Prior of the Initial Pose . 62 3.4.6 Spatial Correlation Models . 62 3.4.7 Implementation . 65 3.4.8 Experimental Results . 67 3.4.9 Conclusion . 72 4 Surface Reconstruction 75 4.1 Introduction . 75 4.2 Direct Polygonal Meshing of Range Images . 79 4.3 Volumetric Surface Reconstruction . 80 4.3.1 Signed Distance Function Estimation . 81 4.3.2 Iso-Surface Triangulation . 84 CONTENTS V 4.4 Surface Optimization . 85 4.4.1 Fitting Potential . 87 4.4.2 Topology Potential . 90 4.4.3 Smoothness Potential . 91 4.4.4 Optimization . 91 4.5 Experimental Results . 92 4.6 Conclusion . 96 5 Photo-Realistic Texture Reconstruction 99 5.1 Introduction . 99 5.1.1 Appearance Models . 99 5.2 Appearance Reconstruction . 102 5.2.1 Reconstruction of Diffuse Texture Maps . 104 5.2.2 Texture Mapping . 104 5.3 Multi-view Texture Reconstruction . 105 5.3.1 Surface Partitioning . 106 5.3.2 Surface Unfolding . 109 5.3.3 Color Reconstruction . 114 5.3.4 Color Interpolation . 117 5.4 Conclusion . 120 6 Results and Applications 123 6.1 Photo-Realistic Reconstruction of Indoor Environments . 123 6.2 Rapid Inspection . 125 6.3 Model Reconstruction for Augmented Reality . 126 7 Summary and Conclusion 129 7.1 Summary . 129 7.2 Conclusion . 131 VI CONTENTS 8 Appendix 133 8.1 Pose Vectors and Rigid Body Transforms . 133 8.1.1 Position . 133 8.1.2 Orientation . 133 8.1.3 Rigid Body Pose . 135 8.1.4 Rigid Body Transforms . 135 8.2 Pose Operations in 3D . 137 8.2.1 The Pose Compounding Operations in 3D . 137 8.2.2 Jacobian of the Pose Compounding Operation . 138 8.2.3 Jacobian of the Inverse Pose Compounding Operation . 139 8.3 Linear Algebra . 140 8.3.1 Solving Linear Systems . 140 8.3.2 Linear Least Squares . 141 CONTENTS VII List of Symbols and Abbreviations General Notation A A matrix with m rows and n columns. 0 1 a11 a12 ··· a1n B a21 a22 ··· a2n C A = B C B . .. C @ . A am1 am2 ··· amn A−1 The inverse of A. AT The transpose of A. kAk Matrix norm (subscript if any denotes what norm). det (A) The determinant of A. The null matrix. Zero in all entries. I The identity matrix. x A n-dimensional vector. 0 1 x1 T Bx2 C x = x x ··· x = B C 1 2 n B . C @ . A xn a · b The dot product of two equal-length vectors a · b = aTb. a × b The cross product of two equal-length vectors. M A set with n elements M = fm1;:::; mng. s A scalar. Statistical Notation P (·) Probability. P (· j ·) Conditional probability. p(·) Probability density. N(µ, σ2) A univariate normal distribution where the parameters µ and σ2 are the mean and the variance. k Nk(µ; Σ) A multivariate normal distribution where the parameters µ 2 R and Σ 2 Rk×k are the mean and the covariance matrix. VIII CONTENTS Acronyms ADC Analog-to-Digital Converter AR Augmented Reality AUV Autonomous Underwater Vehicles BRDF Bidirectional Reflectance Distribution Function BSSRDF Bidirectional Scattering-Surface Reflection Distribution Function CAD Computer-Aided Design CCD Charge-Coupled Device CG Conjugate Gradient CLM Concurrent Localization and Mapping DoF Degree of Freedom EIF Extended Information Filter EKF Extended Kalman Filter HDR High Dynamic Range ICP Iterative Closest Point LIDAR Light Detection And Ranging MAP Maximum A-Posteriori MVS Multi-View Stereo NURBS Non-Uniform Rational B-Spline OPP Orthogonal Procrustes Problem RAG Region Adjacency Graphs RBF Radial Basis Functions RMS Root Mean Square SfM Structure from Motion CONTENTS IX SGD Stochastic Gradient Descent SIFT Scale Invariant Feature Transform SLAM Simultaneous Localization and Mapping SVBRDF Spatial Varying Bidirectional Reflectance Distribution Function 1 Introduction Realistic 3D representations of environments are becoming increasingly important to a wide variety of current and future applications: content creation for com- puter games and geospatial applications, 3D walkthroughs of real estates, digital preservation of cultural heritage and crime sites, and many more.
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