Seamless Texture Mapping of 3D Point Clouds
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Automatic Generation of a 3D City Model
UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA COMPUTER ENGINEERING DEGREE DEGREE FINAL PROJECT Automatic generation of a 3D city model David Murcia Pacheco June, 2017 AUTOMATIC GENERATION OF A 3D CITY MODEL Escuela Superior de Informática UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA Information Technology and Systems SPECIFIC TECHNOLOGY OF COMPUTER ENGINEERING DEGREE FINAL PROJECT Automatic generation of a 3D city model Author: David Murcia Pacheco Director: Dr. Félix Jesús Villanueva Molina June, 2017 David Murcia Pacheco Ciudad Real – Spain E-mail: [email protected] Phone No.:+34 625 922 076 c 2017 David Murcia Pacheco Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". i TRIBUNAL: Presidente: Vocal: Secretario: FECHA DE DEFENSA: CALIFICACIÓN: PRESIDENTE VOCAL SECRETARIO Fdo.: Fdo.: Fdo.: ii Abstract HIS document collects all information related to the Degree Final Project (DFP) of Com- T puter Engineering Degree of the student David Murcia Pacheco, tutorized by Dr. Félix Jesús Villanueva Molina. This work has been developed during 2016 and 2017 in the Escuela Superior de Informática (ESI), in Ciudad Real, Spain. It is based in one of the proposed sub- jects by the faculty of this university for this year, called "Generación automática del modelo en 3D de una ciudad". -
Benchmarks on WWW Performance
The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance Yanshen Sun Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Application Nicholas F. Polys, Chair Doug A. Bowman Peter Sforza Aug 14, 2020 Blacksburg, Virginia Keywords: Point Cloud, WebGL, X3DOM, x3d Copyright 2020, Yanshen Sun The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance Yanshen Sun (ABSTRACT) With the development of remote sensing devices, it becomes more and more convenient for individual researchers to acquire high-resolution point cloud data by themselves. There have been plenty of online tools for the researchers to exhibit their work. However, the drawback of existing tools is that they are not flexible enough for the users to create 3D scenes of a mixture of point-based and triangle-based models. X3DOM is a WebGL-based library built on Extensible 3D (X3D) standard, which enables users to create 3D scenes with only a little computer graphics knowledge. Before X3D 4.0 Specification, little attention has been paid to point cloud rendering in X3DOM. PointProperties, an appearance node newly added in X3D 4.0, provides point size attenuation and texture-color mixing effects to point geometries. In this work, we propose an X3DOM implementation of PointProperties. This implementation fulfills not only the features specified in X3D 4.0 documentation, but other shading effects comparable to the effects of triangle-based geometries in X3DOM, as well as other state-of-the-art point cloud visualization tools. -
Cloudcompare Point Cloud Processing Workshop Daniel Girardeau-Montaut
CloudCompare Point Cloud Processing Workshop Daniel Girardeau-Montaut www.cloudcompare.org @CloudCompareGPL PCP 2019 [email protected] December 4-5, 2019, Stuttgart, Germany Outline About the project Quick overview of the software capabilities Point cloud processing with CloudCompare The project 2003: PhD for EDF R&D EDF Main French power utility More than 150 000 employees worldwide 2 000 @ R&D (< 2%) 200 know about CloudCompare (< 0.2%) Sales >75 B€ > 200 dams + 58 nuclear reactors (19 plants) EDF and Laser Scanning EDF = former owner of Mensi (now Trimble Laser Scanning) Main scanning activity: as-built documentation Scanning a single nuclear reactor building 2002: 3 days, 50 M. points 2014: 1.5 days, 50 Bn points (+ high res. photos) EDF and Laser Scanning Other scanning activities: Building monitoring (dams, cooling towers, etc.) Landslide monitoring Hydrology Historical preservation (EDF Foundation) PhD Change detection on 3D geometric data Application to Emergency Mapping Inspired by 9/11 post-attacks recovery efforts (see “Mapping Ground Zero” by J. Kern, Optech, Nov. 2001) TLS was used for: visualization, optimal crane placement, measurements, monitoring the subsidence of the wreckage pile, slurry wall monitoring, etc. CloudCompare V1 2004-2006 Aim: quickly detecting changes by comparing TLS point clouds… with a CAD mesh or with another (high density) cloud CloudCompare V2 2007: “Industrialization” of CloudCompare … for internal use only! Rationale: idle reactor = 6 M€ / day acquired data -
Openscad User Manual (PDF)
OpenSCAD User Manual Contents 1 Introduction 1.1 Additional Resources 1.2 History 2 The OpenSCAD User Manual 3 The OpenSCAD Language Reference 4 Work in progress 5 Contents 6 Chapter 1 -- First Steps 6.1 Compiling and rendering our first model 6.2 See also 6.3 See also 6.3.1 There is no semicolon following the translate command 6.3.2 See Also 6.3.3 See Also 6.4 CGAL surfaces 6.5 CGAL grid only 6.6 The OpenCSG view 6.7 The Thrown Together View 6.8 See also 6.9 References 7 Chapter 2 -- The OpenSCAD User Interface 7.1 User Interface 7.1.1 Viewing area 7.1.2 Console window 7.1.3 Text editor 7.2 Interactive modification of the numerical value 7.3 View navigation 7.4 View setup 7.4.1 Render modes 7.4.1.1 OpenCSG (F9) 7.4.1.1.1 Implementation Details 7.4.1.2 CGAL (Surfaces and Grid, F10 and F11) 7.4.1.2.1 Implementation Details 7.4.2 View options 7.4.2.1 Show Edges (Ctrl+1) 7.4.2.2 Show Axes (Ctrl+2) 7.4.2.3 Show Crosshairs (Ctrl+3) 7.4.3 Animation 7.4.4 View alignment 7.5 Dodecahedron 7.6 Icosahedron 7.7 Half-pyramid 7.8 Bounding Box 7.9 Linear Extrude extended use examples 7.9.1 Linear Extrude with Scale as an interpolated function 7.9.2 Linear Extrude with Twist as an interpolated function 7.9.3 Linear Extrude with Twist and Scale as interpolated functions 7.10 Rocket 7.11 Horns 7.12 Strandbeest 7.13 Previous 7.14 Next 7.14.1 Command line usage 7.14.2 Export options 7.14.2.1 Camera and image output 7.14.3 Constants 7.14.4 Command to build required files 7.14.5 Processing all .scad files in a folder 7.14.6 Makefile example 7.14.6.1 Automatic -
Robust Tracking and Structure from Motion with Sampling Method
ROBUST TRACKING AND STRUCTURE FROM MOTION WITH SAMPLING METHOD A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS for the degree DOCTOR OF PHILOSOPHY IN ROBOTICS Robotics Institute Carnegie Mellon University Peng Chang July, 2002 Thesis Committee: Martial Hebert, Chair Robert Collins Jianbo Shi Michael Black This work was supported in part by the DARPA Tactical Mobile Robotics program under JPL contract 1200008 and by the Army Research Labs Robotics Collaborative Technology Alliance Contract DAAD 19- 01-2-0012. °c 2002 Peng Chang Keywords: structure from motion, visual tracking, visual navigation, visual servoing °c 2002 Peng Chang iii Abstract Robust tracking and structure from motion (SFM) are fundamental problems in computer vision that have important applications for robot visual navigation and other computer vision tasks. Al- though the geometry of the SFM problem is well understood and effective optimization algorithms have been proposed, SFM is still difficult to apply in practice. The reason is twofold. First, finding correspondences, or "data association", is still a challenging problem in practice. For visual navi- gation tasks, the correspondences are usually found by tracking, which often assumes constancy in feature appearance and smoothness in camera motion so that the search space for correspondences is much reduced. Therefore tracking itself is intrinsically difficult under degenerate conditions, such as occlusions, or abrupt camera motion which violates the assumptions for tracking to start with. Second, the result of SFM is often observed to be extremely sensitive to the error in correspon- dences, which is often caused by the failure of the tracking. This thesis aims to tackle both problems simultaneously. -
Poseray Handbuch 3.10.3
Das PoseRay Handbuch 3.10.3 Zusammengestellt von Steely. Angelehnt an die PoseRay Hilfedatei. PoseRay Handbuch V 3.10.3 Seite 1 Yo! Hör genau zu: Dies ist das deutsche Handbuch zu PoseRay, basierend auf dem Helpfile zum Programm. Es ist keine wörtliche Übersetzung, und FlyerX trifft keine Schuld an diesem Dokument (wenn man davon absieht, daß er PoseRay geschrieben hat). Dies ist ein Handbuch, kein Tutorial. Es erklärt nicht, wie man mit Poser tolle Frauen oder mit POV- Ray tolle Bilder macht. Es ist nur eine freie Übersetzung der poseray.html, die PoseRay beiliegt. Ich will mich bemühen, dieses Dokument aktuell zu halten, und es immer dann überarbeiten und erweitern, wenn FlyerX sichtbar etwas am Programm verändert. Das ist zumindest der Plan. Damit keine Verwirrung aufkommt, folgt das Handbuch in seinen Versionsnummern dem Programm. Die jeweils neueste Version findest Du auf meiner Homepage: www.blackdepth.de. Sei dankbar, daß Schwedenmann und Tom33 von www.POVray-forum.de meinen Prolltext auf Fehler gecheckt haben, sonst wäre das Handbuch noch grausiger. POV-Ray, Poser, DAZ, und viele andere Programm- und Firmennamen in diesem Handbuch sind geschützte Warenzeichen oder zumindest wie solche zu behandeln. Daß kein TM dahinter steht, bedeutet nicht, daß der Begriff frei ist. Unser Markenrecht ist krank, bevor Du also mit den Namen und Begriffen dieses Handbuchs rumalberst, mach dich schlau, ob da einer die Kralle drauf hat. Noch was: dieses Handbuch habe ich geschrieben, es ist mein Werk und ich kann damit machen, was ich will. Deshalb bestimme ich, daß es nicht geschützt ist. Es gibt schon genug Copyright- und IPR- Idioten; ich muß nicht jeden Blödsinn nachmachen. -
POV-Ray Reference
POV-Ray Reference POV-Team for POV-Ray Version 3.6.1 ii Contents 1 Introduction 1 1.1 Notation and Basic Assumptions . 1 1.2 Command-line Options . 2 1.2.1 Animation Options . 3 1.2.2 General Output Options . 6 1.2.3 Display Output Options . 8 1.2.4 File Output Options . 11 1.2.5 Scene Parsing Options . 14 1.2.6 Shell-out to Operating System . 16 1.2.7 Text Output . 20 1.2.8 Tracing Options . 23 2 Scene Description Language 29 2.1 Language Basics . 29 2.1.1 Identifiers and Keywords . 30 2.1.2 Comments . 34 2.1.3 Float Expressions . 35 2.1.4 Vector Expressions . 43 2.1.5 Specifying Colors . 48 2.1.6 User-Defined Functions . 53 2.1.7 Strings . 58 2.1.8 Array Identifiers . 60 2.1.9 Spline Identifiers . 62 2.2 Language Directives . 64 2.2.1 Include Files and the #include Directive . 64 2.2.2 The #declare and #local Directives . 65 2.2.3 File I/O Directives . 68 2.2.4 The #default Directive . 70 2.2.5 The #version Directive . 71 2.2.6 Conditional Directives . 72 2.2.7 User Message Directives . 75 2.2.8 User Defined Macros . 76 3 Scene Settings 81 3.1 Camera . 81 3.1.1 Placing the Camera . 82 3.1.2 Types of Projection . 86 3.1.3 Focal Blur . 88 3.1.4 Camera Ray Perturbation . 89 3.1.5 Camera Identifiers . 89 3.2 Atmospheric Effects . -
Self-Driving Car Autonomous System Overview
Self-Driving Car Autonomous System Overview - Industrial Electronics Engineering - Bachelors’ Thesis - Author: Daniel Casado Herráez Thesis Director: Javier Díaz Dorronsoro, PhD Thesis Supervisor: Andoni Medina, MSc San Sebastián - Donostia, June 2020 Self-Driving Car Autonomous System Overview Daniel Casado Herráez "When something is important enough, you do it even if the odds are not in your favor." - Elon Musk - 2 Self-Driving Car Autonomous System Overview Daniel Casado Herráez To my Grandfather, Family, Friends & to my supervisor Javier Díaz 3 Self-Driving Car Autonomous System Overview Daniel Casado Herráez 1. Contents 1.1. Index 1. Contents ..................................................................................................................................................... 4 1.1. Index.................................................................................................................................................. 4 1.2. Figures ............................................................................................................................................... 6 1.1. Tables ................................................................................................................................................ 7 1.2. Algorithms ........................................................................................................................................ 7 2. Abstract ..................................................................................................................................................... -
Euclidean Reconstruction of Natural Underwater
EUCLIDEAN RECONSTRUCTION OF NATURAL UNDERWATER SCENES USING OPTIC IMAGERY SEQUENCE By Han Hu B.S. in Remote Sensing Science and Technology, Wuhan University, 2005 M.S. in Photogrammetry and Remote Sensing, Wuhan University, 2009 THESIS Submitted to the University of New Hampshire In Partial Fulfillment of The Requirements for the Degree of Master of Science In Ocean Engineering Ocean Mapping September, 2015 This thesis has been examined and approved in partial fulfillment of the requirements for the degree of M.S. in Ocean Engineering Ocean Mapping by: Thesis Director, Yuri Rzhanov, Research Professor, Ocean Engineering Philip J. Hatcher, Professor, Computer Science R. Daniel Bergeron, Professor, Computer Science On May 26, 2015 Original approval signatures are on file with the University of New Hampshire Graduate School. ii ACKNOWLEDGEMENTS I would like to thank all those people who made this thesis possible. My first debt of sincere gratitude and thanks must go to my advisor, Dr. Yuri Rzhanov. Throughout the entire three-year period, he offered his unreserved help, guidance and support to me in both work and life. Without him, it is definite that my thesis could not be completed successfully. I could not have imagined having a better advisor than him. Besides my advisor in Ocean Engineering, I would like to give many thanks to the rest of my thesis committee and my advisors in the Department of Computer Science: Prof. Philip J. Hatcher and Prof. R. Daniel Bergeron as well for their encouragement, insightful comments and all their efforts in making this thesis successful. I also would like to express my thanks to the Center for Coastal and Ocean Mapping and the Department of Computer Science at the University of New Hampshire for offering me the opportunities and environment to work on exciting projects and study in useful courses from which I learned many advanced skills and knowledge. -
Motion Estimation for Self-Driving Cars with a Generalized Camera
2013 IEEE Conference on Computer Vision and Pattern Recognition Motion Estimation for Self-Driving Cars With a Generalized Camera Gim Hee Lee1, Friedrich Fraundorfer2, Marc Pollefeys1 1 Department of Computer Science Faculty of Civil Engineering and Surveying2 ETH Zurich,¨ Switzerland Technische Universitat¨ Munchen,¨ Germany {glee@student, pomarc@inf}.ethz.ch [email protected] Abstract In this paper, we present a visual ego-motion estima- tion algorithm for a self-driving car equipped with a close- to-market multi-camera system. By modeling the multi- camera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the general- ized essential matrix where the full relative motion includ- ing metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera Figure 1. Car with a multi-camera system consisting of 4 cameras correspondences. We show that up to a maximum of 6 so- (front, rear and side cameras in the mirrors). lutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the ready available in some COTS cars, we focus this paper special case with only intra-camera correspondences where on using a multi-camera setup for ego-motion estimation the scale becomes unobservable and provide a practical al- which is one of the key features for self-driving cars. A ternative solution. Our formulation can be efficiently imple- multi-camera setup can be modeled as a generalized cam- mented within RANSAC for robust estimation. -
Texture Mapping There Are Limits to Geometric Modeling
Texture Mapping There are limits to geometric modeling http://www.beinteriordecorator.com National Geographic Although modern GPUs can render millions of triangles/sec, that’s not enough sometimes... Use texture mapping to increase realism through detail Rosalee Wolfe This image is just 8 polygons! [Angel and Shreiner] [Angel and No texture With texture Pixar - Toy Story Store 2D images in buffers and lookup pixel reflectances procedural photo Other uses of textures... [Angel and Shreiner] [Angel and Light maps Shadow maps Environment maps Bump maps Opacity maps Animation 99] [Stam Texture mapping in the OpenGL pipeline • Geometry and pixels have separate paths through pipeline • meet in fragment processing - where textures are applied • texture mapping applied at end of pipeline - efficient since relatively few polygons get past clipper uv Mapping Tschmits Wikimedia Commons • 2D texture is parameterized by (u,v) • Assign polygon vertices texture coordinates • Interpolate within polygon Texture Calibration Cylindrical mapping (x,y,z) -> (theta, h) -> (u,v) [Rosalee Wolfe] Spherical Mapping (x,y,z) -> (latitude,longitude) -> (u,v) [Rosalee Wolfe] Box Mapping [Rosalee Wolfe] Parametric Surfaces 32 parametric patches 3D solid textures [Dong et al., 2008] al., et [Dong can map object (x,y,z) directly to texture (u,v,w) Procedural textures Rosalee Wolfe e.g., Perlin noise Triangles Texturing triangles • Store (u,v) at each vertex • interpolate inside triangles using barycentric coordinates Texture Space Object Space v 1, 1 (u,v) = (0.2, 0.8) -
Texture Builder Plugin for Cambam
Texture Builder Plugin for CamBam [Version 1.0.1] Purpose Textured surfaces are commonly used in CNC machining to create interesting or contrasting backgrounds on carved items. Essentially a textured surface suitable for CNC machining is a 2.5D surface with a Z (depth) varying over an X-Y plane. This plugin is built on the following premises: That the surface to be textured is a tessellation of a series of 2.5D tiles. Each tile can be repeated over the surface using some combination of: o Copying o Translating o Scaling o Repeating on an X-Y grid, or around a circular arc in the X-Y plane. The tile element must be predefined (using some other tool) as: o a height cloud (a set of X,Y,Z coordinate points) in a CSV file, o an STL model (Sterolithographic file, in ASCII or Binary formats), o a RAW file (sets of X,Y,Z point triplets defining each surface triangular surface patch, as defined for CamBam, in ASCII format), or o an image file (BMP. GIF, JPG, PNG or TIFF formatted) where the grey scale values are to be interpreted as a height map (in the range 0 to 255). Once the scene is constructed, the complete scene surface can be saved as a XYZ height cloud, an STL file or a RAW file, for input into CamBam, or other CAM modellers. Related Tools and Potential Contributions To build a tile element to form the required texture various support tools can be used to help, each performing a particular task in the process.