How To Create A Surface From Points On A Computer With A Marching Cube
|
|
- Amie Robertson
- 3 years ago
- Views:
Transcription
1 Surface Reconstruction from a Point Cloud with Normals Landon Boyd and Massih Khorvash Department of Computer Science University of British Columbia,2366 Main Mall Vancouver, BC, V6T1Z4, Canada {blandon,khorvash}@cs.ubc.ca Abstract We set out to implement a tool to reconstruct a 3-D triangle mesh from a set of points and the corresponding normals of an unknown surface. This surface may have arbitrarily complex topology and geometry, and may contain boundaries. We base our implementation on an influential reconstruction paper [HDD 92]. Results were generated for a variety of challenging inputs. 1. Introduction Hoppe et al. [HDD 92] describe an algorithm to reconstruct simplicial surfaces from a set of points, as one would expect from the output of a laser range scanner. Although the use of Marching Cubes [LC87] for surface reconstruction was not new, their work was groundbreaking in its handling of arbitrary topology and boundaries. As such, the work has been cited by a vast number of surface reconstruction papers (1465 citations on Google Scholar as of Dec. 10, 2008). We chose to implement this algorithm as we believed it would give us insight into the core ideas underlying many state-of-the-art surface reconstruction techniques. Taking a set of points on the unknown surface with their normals allowed us to simplify the algorithm. The rest of this report is organized as follows. Section 2 describes the algorithm. In section 3, we describe how we implemented the algorithm. Section 4 shows the results obtained. In section 5 we draw conclusions and suggest some possible improvements. 2. Algorithm Hoppe et al. s algorithm has two phases and a refinement step as follows. Phase 1: Calculate a tangent plane for each sample point Determine a globally consistent orientation for the normals of the tangent planes Phase 2: Extract an isosurface using Marching Cubes [LC87] with the signed distance function Refinement step: Improve the quality of the resulting mesh by collapsing edges of triangles with undesirable aspect ratios For this project, we are given the normals at each of the input points, which makes the first phase unnecessary. The signed distance function is as shown in algorithm 1. In other words, the distance function returns the distance of each marching cube vertex to the tangent plane of the nearest sample point, with the following exception: if the projected point, z, is too far away from any sample points (given the sampling density), the distance function returns undefined. When a marching cube has an undefined value at any of its vertices, no surface is generated within that cube. That way, the algorithm can reconstruct surfaces with boundaries Algorithm Complexity The subproblems of extracting the isosurface by marching cubes are:
2 2 Input: A marching cube vertex p, Sampling density ρ SIGNED-DISTANCE-FUNCTION(p, ρ): begin o the nearest sample point to p ˆn the normal at o z the projection of p onto the tangent plane defined by o and ˆn d the distance to the sample point nearest to z if d < ρ then return (p o) ˆn else return undefined Algorithm 1: Definition of the signed distance function Determining the nearest sample input point to each marching cube vertex, p. Determining the nearest sample input point to each projected marching cube vertex, z. Both steps require finding the nearest sample in the point cloud to a given point. By using a k-d tree [Ben80], the resultant time complexity is O(log(n)), where n is the number of points in the cloud. Hence, the time complexity of our algorithm is O(mlog(n)), where m is the number of marching cubes. The empirical results are shown in section Implementation Implementation was divided into following subtasks. Generate point and normal data Read points and normals into a data structure Implement the signed distance function Integrate contour tracing using marching cubes Post-process to eliminate undesirable triangles For input data, we used freely available 3-D models from various sources. We implemented a tool in Graphite which extracted the vertices and their normals from a model and wrote them to a file with a.cloud extension. We exted Graphite to read these.cloud files containing point and normal data, by implementing a PointCloud class exting OGF::Grob ( GRaphite OBject ). This way, from the user s point of view, loading point cloud is as easy as loading any other type of file. When the user selects a.cloud file, a PointCloud object is created and its load() member function is called to parse the data. Because the signed distance function requires finding the nearest neighbor amongst points in the cloud, it was important that the points be stored in a data structure allowing this for instance, search to be performed efficiently. We used the k-d tree implementation, libkdtree++, by Martin F. Krafft. Due to its effective use of C++ templates, we did not have to change a line of code in that library; we only had to implement a trivial functor class to allow comparison between the PointNormal objects stored in the structure. The biggest decision regarding the signed distance function was determination of ρ, the sampling density. In a real world scenario, this would be a property of the range scanner. By default, we used the maximum distance between any point and its nearest neighbor, as anything less could result in extraneous boundaries. We provided a user interface to allow the default to be overridden and also provided an option to help the user choose ρ by presenting a histogram of the nearest neighbor distances for all sample points. For contour tracing, we started with the MarchingCubes class by Josh Grant. It was implemented for the Open Inventor environment, so we first updated the code to work with Graphite by removing any Open Inventor specific code, defining a ScalarField class and using OGF::Vector3d instead of the Open Inventor equivalent. We also updated MarchingCubes to take a ScalarField of pairs, the first element of the pair containing the value of the distance function (at a marching cube vertex) and the second element, a boolean indicating whether the value is defined in the sense described in section 2. Additional logic was added to avoid generating triangles for cubes with undefined values for any of their vertices, thus allowing the reconstruction of boundaries. When we ran marching cubes against a model of Homer, the resulting mesh was shorter and fatter than the input. This was because MarchingCubes always made its grid fit exactly within a bounding box, even if there were an unequal number of cubes along the three axes. We updated MarchingCubes to always use grid cells of equal length on all sides (i.e. cubes ), with only the cubes along the longest axis adding up to 1 unit length. The final step was to post-process the reconstructed mesh to remove unsightly triangles generated by marching cubes. We used a mesh simplifier based on the quadric error metric [GH97] and let the user decide how much simplification results in acceptable mesh quality. 4. Results We were impressed by the results. Our algorithm did reconstruct models with complex topology and geometry, and also reconstructed boundaries. When it was faced with inadequate data, it still produced a plausible mesh. implemented for assignment 2
3 3 To test the ability of our algorithm to reconstruct a basic surface with a boundary, we tried it on an ellipsoid cloud consisting of 3387 points, with a hole cut out of one (figure 1). from a 40,000 vertex chair model. This was a challenging model to reproduce for a number of reasons. In addition to the high genus, it also has relatively fine detail in the form of thin rods that make up its back and legs and a thin, flat seat. The original mesh and resulting reconstruction are shown in figure 4. Figure 1: ellipsoid point cloud with normals in green The boundary of the original ellipsoid model was reconstructed as shown in figure 2. Figure 4: original chair model (left) and reconstruction (right) Figure 2: original ellipsoid mesh (left) and generated mesh (right) To see how the boundary was reproduced, it s instructive to look at the marching cube values. In figure 3, we show the marching cube vertices with values less than 0 (yellow) and vertices with undefined values (pink). In the area of the hole, the signed distance function produces undefined values, which prevents a surface from being generated in those cubes, resulting in the desired boundary. The resulting mesh has no boundaries and reproduces the original shape well, though some additional holes are introduced in the seat. Some stair step artifacts can also be seen on parts of the seat back. We believe the reason for the holes in the seat is a more pronounced stair stepping effect, because of the angle of the seat in relation to the marching cubes (figure 5). Figure 3: inside and undefined marching cube vertices To test complex topology, we used a point cloud generated Figure 5: chair orientation, close up of stair step effect on seat
4 4 A geometrically complex knot was handled particularly well, as shown in figure 6. Figure 6: knot point cloud (left) and reconstruction (right) The Homer point cloud had a few undersampled regions which revealed themselves after we added boundary reproduction to our code. figure 7 shows the output mesh with boundaries. Figure 8: Marching cube projections of Homer not practical in the case of Homer. Another option is to disable boundary detection. By choosing this option in the user interface, the mesh is generated which captures all of the detail but does not have boundaries. This is an acceptable solution for Homer because there are no boundaries desired in the surface. However, this may not always be the case. The best option, when possible, would be to fill in the undersampled region with reasonable sample points. Figure 7: Homer with boundaries As expected, the mesh quality produced by marching cubes is less than ideal. As shown with the bunny model in figure 9, there are noticeable artifacts, seen as contours of the distance function. Hoppe et al. suggest collapsing edges using a priority queue sorted by the aspect ratio of triangles. We obtained good results by collapsing edges according to the quadric error metric [GH97]. In figure 9, we also show the result of simplification to various mesh sizes and clearly the artifacts are eliminated without much detriment to the level of detail in the mesh. We are able to visualize the undersampling in the point cloud by viewing the projections of certain marching cube vertices onto the tangent planes of their nearest points (figure 8). It can be seen that the projected points (z in the algorithm) are far away from any sample points, resulting in the boundary. When faced with this situation, the user has a few options. One is to reduce the specified sampling density; this makes the algorithm more tolerant to undersampling, but also results in less detail in the resultant surface. This was Figure 9: generated bunny mesh (7k vertices, left), simplified to 6k (middle) and 5k (right) To evaluate the performance of our implementation, we reconstructed the hand point cloud in figure 10 consisting of points.
5 5 Figure 10: hand point cloud (left), marching cubes (middle) and reconstructed surface (right) [KBSS01] KOBBELT L. P., BOTSCH M., SCHWANECKE U., SEIDEL H.-P.: Feature sensitive surface extraction from volume data. In SIGGRAPH 01: Proceedings of the 28th annual conference on Computer graphics and interactive techniques (New York, NY, USA, 2001), ACM, pp [LC87] LORENSEN W. E., CLINE H. E.: Marching cubes: A high resolution 3d surface construction algorithm. SIG- GRAPH Comput. Graph. 21, 4 (1987), On a 3.2GHz Pentium 4, reconstruction of the 40,000 vertex output model took 33 minutes. 5. Conclusion We implemented a variant of a fundamental reconstruction algorithm by Hoppe et al., which is the basis of many other reconstruction algorithms. We tested our implementation on several geometrically and topologically complex models, and also some models with boundaries. Rather than using only an unorganized point cloud, we used a cloud of points with their normals. We also assumed noiseless data. Our algorithm reconstructed impressive results in a reasonable time, the most noticeable limitation being stair step artifacts on thin features not aligned with the marching cube grid. The performance could be improved by only considering cubes in the region of the surface, using a technique such as adaptive octree refinement [KBSS01]. Also, it might be possible to improve performance by using marching cubes larger than the sampling density. References [Ben80] BENTLEY J. L.: Multidimensional divide-andconquer. Commun. ACM 23, 4 (1980), [GH97] GARLAND M., HECKBERT P. S.: Surface simplification using quadric error metrics. In SIGGRAPH 97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques (New York, NY, USA, 1997), ACM Press/Addison-Wesley Publishing Co., pp [HDD 92] HOPPE H., DEROSE T., DUCHAMP T., MC- DONALD J., STUETZLE W.: Surface reconstruction from unorganized points. In SIGGRAPH 92: Proceedings of the 19th annual conference on Computer graphics and interactive techniques (New York, NY, USA, 1992), ACM, pp
Dual Marching Cubes: Primal Contouring of Dual Grids
Dual Marching Cubes: Primal Contouring of Dual Grids Scott Schaefer and Joe Warren Rice University 6100 Main St. Houston, TX 77005 sschaefe@rice.edu and jwarren@rice.edu Abstract We present a method for
More informationEfficient Storage, Compression and Transmission
Efficient Storage, Compression and Transmission of Complex 3D Models context & problem definition general framework & classification our new algorithm applications for digital documents Mesh Decimation
More informationA unified representation for interactive 3D modeling
A unified representation for interactive 3D modeling Dragan Tubić, Patrick Hébert, Jean-Daniel Deschênes and Denis Laurendeau Computer Vision and Systems Laboratory, University Laval, Québec, Canada [tdragan,hebert,laurendeau]@gel.ulaval.ca
More informationFeature Sensitive Surface Extraction from Volume Data
Feature Sensitive Surface Extraction from Volume Data Leif P. Kobbelt Mario Botsch Ulrich Schwanecke Hans-Peter Seidel Computer Graphics Group, RWTH-Aachen Computer Graphics Group, MPI Saarbrücken Figure
More informationSegmentation of building models from dense 3D point-clouds
Segmentation of building models from dense 3D point-clouds Joachim Bauer, Konrad Karner, Konrad Schindler, Andreas Klaus, Christopher Zach VRVis Research Center for Virtual Reality and Visualization, Institute
More informationParallel Simplification of Large Meshes on PC Clusters
Parallel Simplification of Large Meshes on PC Clusters Hua Xiong, Xiaohong Jiang, Yaping Zhang, Jiaoying Shi State Key Lab of CAD&CG, College of Computer Science Zhejiang University Hangzhou, China April
More informationFaculty of Computer Science Computer Graphics Group. Final Diploma Examination
Faculty of Computer Science Computer Graphics Group Final Diploma Examination Communication Mechanisms for Parallel, Adaptive Level-of-Detail in VR Simulations Author: Tino Schwarze Advisors: Prof. Dr.
More informationPCL - SURFACE RECONSTRUCTION
PCL - SURFACE RECONSTRUCTION TOYOTA CODE SPRINT Alexandru-Eugen Ichim Computer Graphics and Geometry Laboratory PROBLEM DESCRIPTION 1/2 3D revolution due to cheap RGB-D cameras (Asus Xtion & Microsoft
More informationLecture 7 - Meshing. Applied Computational Fluid Dynamics
Lecture 7 - Meshing Applied Computational Fluid Dynamics Instructor: André Bakker http://www.bakker.org André Bakker (2002-2006) Fluent Inc. (2002) 1 Outline Why is a grid needed? Element types. Grid types.
More informationModel Repair. Leif Kobbelt RWTH Aachen University )NPUT $ATA 2EMOVAL OF TOPOLOGICAL AND GEOMETRICAL ERRORS !NALYSIS OF SURFACE QUALITY
)NPUT $ATA 2ANGE 3CAN #!$ 4OMOGRAPHY 2EMOVAL OF TOPOLOGICAL AND GEOMETRICAL ERRORS!NALYSIS OF SURFACE QUALITY 3URFACE SMOOTHING FOR NOISE REMOVAL 0ARAMETERIZATION 3IMPLIFICATION FOR COMPLEXITY REDUCTION
More informationOff-line Model Simplification for Interactive Rigid Body Dynamics Simulations Satyandra K. Gupta University of Maryland, College Park
NSF GRANT # 0727380 NSF PROGRAM NAME: Engineering Design Off-line Model Simplification for Interactive Rigid Body Dynamics Simulations Satyandra K. Gupta University of Maryland, College Park Atul Thakur
More informationA typical 3D modeling system involves the phases of 1. Individual range image acquisition from different viewpoints.
Efficient Model Creation of Large Structures based on Range Segmentation 2nd International Symposium on 3D Data Processing, Visualization & Transmission, September 2004, Thessaloniki, Greece. Ioannis Stamos
More informationDelaunay Based Shape Reconstruction from Large Data
Delaunay Based Shape Reconstruction from Large Data Tamal K. Dey Joachim Giesen James Hudson Ohio State University, Columbus, OH 4321, USA Abstract Surface reconstruction provides a powerful paradigm for
More informationFrom Scattered Samples to Smooth Surfaces
From Scattered Samples to Smooth Surfaces Kai Hormann 1 California Institute of Technology (a) (b) (c) (d) Figure 1: A point cloud with 4,100 scattered samples (a), its triangulation with 7,938 triangles
More informationHow To Draw In Autocad
DXF Import and Export for EASE 4.0 Page 1 of 9 DXF Import and Export for EASE 4.0 Bruce C. Olson, Dr. Waldemar Richert ADA Copyright 2002 Acoustic Design Ahnert EASE 4.0 allows both the import and export
More informationAnimated Models Simplification with Local Area Distortion and Deformation Degree Control
Volume 1, Number 1, September 2014 JOURNAL OF COMPUTER SCIENCE AND SOFTWARE APPLICATION Animated Models Simplification with Local Area Distortion and Deformation Degree Control Shixue Zhang* Changchun
More informationNormal Estimation for Point Clouds: A Comparison Study for a Voronoi Based Method
Eurographics Symposium on Point-Based Graphics (2005) M. Pauly, M. Zwicker, (Editors) Normal Estimation for Point Clouds: A Comparison Study for a Voronoi Based Method Tamal K. Dey Gang Li Jian Sun The
More informationA 3d particle visualization system for temperature management
A 3d particle visualization system for temperature management Benoit Lange, Nancy Rodriguez, William Puech, Hervé Rey, Xavier Vasques To cite this version: Benoit Lange, Nancy Rodriguez, William Puech,
More informationA Generalized Marching Cubes Algorithm Based On Non-Binary Classifications
Konrad-Zuse-Zentrum fu r Informationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Germany HANS-CHRISTIAN HEGE DETLEV STALLING MARTIN SEEBASS MALTE ZOCKLER A Generalized Marching Cubes Algorithm Based
More informationRobust NURBS Surface Fitting from Unorganized 3D Point Clouds for Infrastructure As-Built Modeling
81 Robust NURBS Surface Fitting from Unorganized 3D Point Clouds for Infrastructure As-Built Modeling Andrey Dimitrov 1 and Mani Golparvar-Fard 2 1 Graduate Student, Depts of Civil Eng and Engineering
More informationVolume visualization I Elvins
Volume visualization I Elvins 1 surface fitting algorithms marching cubes dividing cubes direct volume rendering algorithms ray casting, integration methods voxel projection, projected tetrahedra, splatting
More informationConstrained Tetrahedral Mesh Generation of Human Organs on Segmented Volume *
Constrained Tetrahedral Mesh Generation of Human Organs on Segmented Volume * Xiaosong Yang 1, Pheng Ann Heng 2, Zesheng Tang 3 1 Department of Computer Science and Technology, Tsinghua University, Beijing
More informationAdaptive Fourier-Based Surface Reconstruction
Adaptive Fourier-Based Surface Reconstruction Oliver Schall, Alexander Belyaev, and Hans-Peter Seidel Computer Graphics Group Max-Planck-Institut für Informatik Stuhlsatzenhausweg 85, 66123 Saarbrücken,
More informationEfficient Simplification of Point-Sampled Surfaces
Efficient Simplification of Point-Sampled Surfaces Mark Pauly Markus Gross Leif P Kobbelt ETH Zürich ETH Zürich RWTH Aachen Figure 1: Michelangelo s David at different levels-of-detail From left to right,
More informationTopographic Change Detection Using CloudCompare Version 1.0
Topographic Change Detection Using CloudCompare Version 1.0 Emily Kleber, Arizona State University Edwin Nissen, Colorado School of Mines J Ramón Arrowsmith, Arizona State University Introduction CloudCompare
More informationDual Contouring of Hermite Data
Dual Contouring of Hermite Data Tao Ju, Frank Losasso, Scott Schaefer, Joe Warren Rice University Figure 1: A temple undergoing destructive modifications. Both models were generated by dual contouring
More informationRobust Blind Watermarking Mechanism For Point Sampled Geometry
Robust Blind Watermarking Mechanism For Point Sampled Geometry Parag Agarwal Balakrishnan Prabhakaran Department of Computer Science, University of Texas at Dallas MS EC 31, PO Box 830688, Richardson,
More information3D Visualization of particle system with extracted data from sensor
Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 3-5, 2011,
More informationInformatica Universiteit van Amsterdam
Bachelor Informatica Informatica Universiteit van Amsterdam Volumetric Comparison of Simplified 3D Models Steven Klein June 12, 2012 Supervisor: Robert Belleman (UvA) Abstract In the field of computer
More informationHowTo Rhino & ICEM. 1) New file setup: choose Millimeter (automatically converts to Meters if imported to ICEM)
HowTo Rhino & ICEM Simple 2D model 1) New file setup: choose Millimeter (automatically converts to Meters if imported to ICEM) 2) Set units: File Properties Units: Model units: should already be Millimeters
More informationCOMPARING TECHNIQUES FOR TETRAHEDRAL MESH GENERATION
COMPARING TECHNIQUES FOR TETRAHEDRAL MESH GENERATION M. A. S. Lizier Instituto de Ciências Matemáticas e de Computação - Universidade de São Paulo, Brazil lizier@icmc.usp.br J. F. Shepherd Sandia National
More informationVisualization methods for patent data
Visualization methods for patent data Treparel 2013 Dr. Anton Heijs (CTO & Founder) Delft, The Netherlands Introduction Treparel can provide advanced visualizations for patent data. This document describes
More informationVolumetric Meshes for Real Time Medical Simulations
Volumetric Meshes for Real Time Medical Simulations Matthias Mueller and Matthias Teschner Computer Graphics Laboratory ETH Zurich, Switzerland muellerm@inf.ethz.ch, http://graphics.ethz.ch/ Abstract.
More informationCharacter Animation from 2D Pictures and 3D Motion Data ALEXANDER HORNUNG, ELLEN DEKKERS, and LEIF KOBBELT RWTH-Aachen University
Character Animation from 2D Pictures and 3D Motion Data ALEXANDER HORNUNG, ELLEN DEKKERS, and LEIF KOBBELT RWTH-Aachen University Presented by: Harish CS-525 First presentation Abstract This article presents
More informationFast and Robust Normal Estimation for Point Clouds with Sharp Features
1/37 Fast and Robust Normal Estimation for Point Clouds with Sharp Features Alexandre Boulch & Renaud Marlet University Paris-Est, LIGM (UMR CNRS), Ecole des Ponts ParisTech Symposium on Geometry Processing
More informationTutorial: 2D Pipe Junction Using Hexa Meshing
Tutorial: 2D Pipe Junction Using Hexa Meshing Introduction In this tutorial, you will generate a mesh for a two-dimensional pipe junction, composed of two inlets and one outlet. After generating an initial
More informationTeaching Algorithms and Data Structures through Graphics
EUROGRAPHICS 2007 Education Papers Teaching Algorithms and Data Structures through Graphics Andrew T. Duchowski and Timothy A. Davis School of Computing, Clemson University, Clemson, SC, USA Abstract This
More informationIntersection of a Line and a Convex. Hull of Points Cloud
Applied Mathematical Sciences, Vol. 7, 213, no. 13, 5139-5149 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ams.213.37372 Intersection of a Line and a Convex Hull of Points Cloud R. P. Koptelov
More informationHistoPyramid stream compaction and expansion
HistoPyramid stream compaction and expansion Christopher Dyken1 * and Gernot Ziegler2 Advanced Computer Graphics / Vision Seminar TU Graz 23/10-2007 1 2 University of Oslo Max-Planck-Institut fu r Informatik,
More informationGUIDE TO POST-PROCESSING OF THE POINT CLOUD
GUIDE TO POST-PROCESSING OF THE POINT CLOUD Contents Contents 3 Reconstructing the point cloud with MeshLab 16 Reconstructing the point cloud with CloudCompare 2 Reconstructing the point cloud with MeshLab
More informationTutorial: 3D Pipe Junction Using Hexa Meshing
Tutorial: 3D Pipe Junction Using Hexa Meshing Introduction In this tutorial, you will generate a mesh for a three-dimensional pipe junction. After checking the quality of the first mesh, you will create
More informationConsolidated Visualization of Enormous 3D Scan Point Clouds with Scanopy
Consolidated Visualization of Enormous 3D Scan Point Clouds with Scanopy Claus SCHEIBLAUER 1 / Michael PREGESBAUER 2 1 Institute of Computer Graphics and Algorithms, Vienna University of Technology, Austria
More informationOutdoor beam tracing over undulating terrain
Outdoor beam tracing over undulating terrain Bram de Greve, Tom De Muer, Dick Botteldooren Ghent University, Department of Information Technology, Sint-PietersNieuwstraat 4, B-9000 Ghent, Belgium, {bram.degreve,tom.demuer,dick.botteldooren}@intec.ugent.be,
More informationBernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA
Are Image Quality Metrics Adequate to Evaluate the Quality of Geometric Objects? Bernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA ABSTRACT
More informationDeriving Camera and Point Location From a Series of Photos Using Numerical Optimization
Deriving Camera and Point Location From a Series of Photos Using Numerical Optimization by Chris Studholme Abstract The goal of this project is to discover what attributes of a 3 dimensional scene can
More informationAn Open Framework for Reverse Engineering Graph Data Visualization. Alexandru C. Telea Eindhoven University of Technology The Netherlands.
An Open Framework for Reverse Engineering Graph Data Visualization Alexandru C. Telea Eindhoven University of Technology The Netherlands Overview Reverse engineering (RE) overview Limitations of current
More informationModelling 3D Avatar for Virtual Try on
Modelling 3D Avatar for Virtual Try on NADIA MAGNENAT THALMANN DIRECTOR MIRALAB UNIVERSITY OF GENEVA DIRECTOR INSTITUTE FOR MEDIA INNOVATION, NTU, SINGAPORE WWW.MIRALAB.CH/ Creating Digital Humans Vertex
More informationRIEGL VZ-400 NEW. Laser Scanners. Latest News March 2009
Latest News March 2009 NEW RIEGL VZ-400 Laser Scanners The following document details some of the excellent results acquired with the new RIEGL VZ-400 scanners, including: Time-optimised fine-scans The
More informationIntroduction to ANSYS
Lecture 3 Introduction to ANSYS Meshing 14. 5 Release Introduction to ANSYS Meshing 2012 ANSYS, Inc. March 27, 2014 1 Release 14.5 Introduction to ANSYS Meshing What you will learn from this presentation
More information3D Building Roof Extraction From LiDAR Data
3D Building Roof Extraction From LiDAR Data Amit A. Kokje Susan Jones NSG- NZ Outline LiDAR: Basics LiDAR Feature Extraction (Features and Limitations) LiDAR Roof extraction (Workflow, parameters, results)
More informationVisualisation in the Google Cloud
Visualisation in the Google Cloud by Kieran Barker, 1 School of Computing, Faculty of Engineering ABSTRACT Providing software as a service is an emerging trend in the computing world. This paper explores
More informationSpatio-Temporal Mapping -A Technique for Overview Visualization of Time-Series Datasets-
Progress in NUCLEAR SCIENCE and TECHNOLOGY, Vol. 2, pp.603-608 (2011) ARTICLE Spatio-Temporal Mapping -A Technique for Overview Visualization of Time-Series Datasets- Hiroko Nakamura MIYAMURA 1,*, Sachiko
More informationNeural Gas for Surface Reconstruction
Neural Gas for Surface Reconstruction Markus Melato, Barbara Hammer, Kai Hormann IfI Technical Report Series IfI-07-08 Impressum Publisher: Institut für Informatik, Technische Universität Clausthal Julius-Albert
More informationComputational Geometry. Lecture 1: Introduction and Convex Hulls
Lecture 1: Introduction and convex hulls 1 Geometry: points, lines,... Plane (two-dimensional), R 2 Space (three-dimensional), R 3 Space (higher-dimensional), R d A point in the plane, 3-dimensional space,
More informationSurface Reconstruction from Point Clouds
BOURNEMOUTH UNIVERSITY Surface Reconstruction from Point Clouds Master Thesis Navpreet Kaur Pawar M.Sc. Computer Animation and Visual Effects Supervisor: - Jon Macey 15-AUG-2013 ABSTRACT Recent advancement
More informationDESIGN, TRANSFORMATION AND ANIMATION OF HUMAN FACES
DESIGN, TRANSFORMATION AND ANIMATION OF HUMAN FACES N.Magnenat-Thalmann, H.T.Minh, M.de Angelis, D.Thalmann Abstract Creation of new human faces for synthetic actors is a tedious and painful task. The
More informationIntroduction to ANSYS ICEM CFD
Workshop 8.2 3D Pipe Junction 14.5 Release Introduction to ANSYS ICEM CFD 2012 ANSYS, Inc. April 1, 2013 1 Release 14.5 3D Pipe Junction 3D Pipe Junction This is a simple 4-way pipe intersection with two
More informationReflection and Refraction
Equipment Reflection and Refraction Acrylic block set, plane-concave-convex universal mirror, cork board, cork board stand, pins, flashlight, protractor, ruler, mirror worksheet, rectangular block worksheet,
More informationSelf-Positioning Handheld 3D Scanner
Self-Positioning Handheld 3D Scanner Method Sheet: How to scan in Color and prep for Post Processing ZScan: Version 3.0 Last modified: 03/13/2009 POWERED BY Background theory The ZScanner 700CX was built
More informationAvizo Inspect New software for industrial inspection and materials R&D
Avizo Inspect New software for industrial inspection and materials R&D Reduce your design cycle, inspection times, and meet higher-level quality standards at a lower cost. Avizo Inspect software streamlines
More informationCUBE-MAP DATA STRUCTURE FOR INTERACTIVE GLOBAL ILLUMINATION COMPUTATION IN DYNAMIC DIFFUSE ENVIRONMENTS
ICCVG 2002 Zakopane, 25-29 Sept. 2002 Rafal Mantiuk (1,2), Sumanta Pattanaik (1), Karol Myszkowski (3) (1) University of Central Florida, USA, (2) Technical University of Szczecin, Poland, (3) Max- Planck-Institut
More informationGlass coloured glass may pick up on scan. Top right of screen tabs: these tabs will relocate lost windows.
Artec 3D scanner Instructions for Medium Handheld (MH) Scanner Scanning Conditions: Objects/surfaces that don t scan well: Black or shiny objects and objects with sharp edges or points, hair, glass, transparent
More informationHigher Education Math Placement
Higher Education Math Placement Placement Assessment Problem Types 1. Whole Numbers, Fractions, and Decimals 1.1 Operations with Whole Numbers Addition with carry Subtraction with borrowing Multiplication
More informationBOĞAZİÇİ UNIVERSITY COMPUTER ENGINEERING
Parallel l Tetrahedral Mesh Refinement Mehmet Balman Computer Engineering, Boğaziçi University Adaptive Mesh Refinement (AMR) A computation ti technique used to improve the efficiency i of numerical systems
More informationA. OPENING POINT CLOUDS. (Notepad++ Text editor) (Cloud Compare Point cloud and mesh editor) (MeshLab Point cloud and mesh editor)
MeshLAB tutorial 1 A. OPENING POINT CLOUDS (Notepad++ Text editor) (Cloud Compare Point cloud and mesh editor) (MeshLab Point cloud and mesh editor) 2 OPENING POINT CLOUDS IN NOTEPAD ++ Let us understand
More information1. Abstract 2. Introduction 3. Algorithms and Techniques
MS PROJECT Virtual Surgery Piyush Soni under the guidance of Dr. Jarek Rossignac, Brian Whited Georgia Institute of Technology, Graphics, Visualization and Usability Center Atlanta, GA piyush_soni@gatech.edu,
More informationSolving Simultaneous Equations and Matrices
Solving Simultaneous Equations and Matrices The following represents a systematic investigation for the steps used to solve two simultaneous linear equations in two unknowns. The motivation for considering
More informationLecture 9: Geometric map transformations. Cartographic Transformations
Cartographic Transformations Analytical and Computer Cartography Lecture 9: Geometric Map Transformations Attribute Data (e.g. classification) Locational properties (e.g. projection) Graphics (e.g. symbolization)
More informationMedial Axis Construction and Applications in 3D Wireless Sensor Networks
Medial Axis Construction and Applications in 3D Wireless Sensor Networks Su Xia, Ning Ding, Miao Jin, Hongyi Wu, and Yang Yang Presenter: Hongyi Wu University of Louisiana at Lafayette Outline Introduction
More informationIn mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data.
MATHEMATICS: THE LEVEL DESCRIPTIONS In mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data. Attainment target
More informationNew York State Student Learning Objective: Regents Geometry
New York State Student Learning Objective: Regents Geometry All SLOs MUST include the following basic components: Population These are the students assigned to the course section(s) in this SLO all students
More informationMODERN VOXEL BASED DATA AND GEOMETRY ANALYSIS SOFTWARE TOOLS FOR INDUSTRIAL CT
MODERN VOXEL BASED DATA AND GEOMETRY ANALYSIS SOFTWARE TOOLS FOR INDUSTRIAL CT C. Reinhart, C. Poliwoda, T. Guenther, W. Roemer, S. Maass, C. Gosch all Volume Graphics GmbH, Heidelberg, Germany Abstract:
More informationCATIA V5R21 - FACT SHEET
CATIA V5R21 - FACT SHEET Introduction What s New at a Glance Overview Detailed Description INTRODUCTION CATIA V5 is the leading solution for product success. It addresses all manufacturing organizations;
More informationTopological Data Analysis Applications to Computer Vision
Topological Data Analysis Applications to Computer Vision Vitaliy Kurlin, http://kurlin.org Microsoft Research Cambridge and Durham University, UK Topological Data Analysis quantifies topological structures
More informationSECONDARY STORAGE TERRAIN VISUALIZATION IN A CLIENT-SERVER ENVIRONMENT: A SURVEY
SECONDARY STORAGE TERRAIN VISUALIZATION IN A CLIENT-SERVER ENVIRONMENT: A SURVEY Kai Xu and Xiaofang Zhou School of Information Technology and Electrical Engineering The University of Queensland, Brisbane,
More informationTHE ALGORITHMIC AUDITORIUM. A computational model for auditorium design. 1. Introduction
THE ALGORITHMIC AUDITORIUM A computational model for auditorium design GANAPATHY MAHALINGAM Department of Architecture and Landscape Architecture North Dakota State University Fargo, North Dakota USA Abstract.
More informationAlgebra Geometry Glossary. 90 angle
lgebra Geometry Glossary 1) acute angle an angle less than 90 acute angle 90 angle 2) acute triangle a triangle where all angles are less than 90 3) adjacent angles angles that share a common leg Example:
More informationOn Fast Surface Reconstruction Methods for Large and Noisy Point Clouds
On Fast Surface Reconstruction Methods for Large and Noisy Point Clouds Zoltan Csaba Marton, Radu Bogdan Rusu, Michael Beetz Intelligent Autonomous Systems, Technische Universität München {marton,rusu,beetz}@cs.tum.edu
More informationPoint Cloud Segmentation via Constrained Nonlinear Least Squares Surface Normal Estimates
Point Cloud Segmentation via Constrained Nonlinear Least Squares Surface Normal Estimates Edward Castillo Radiation Oncology Department University of Texas MD Anderson Cancer Center, Houston TX ecastillo3@mdanderson.org
More informationPro/ENGINEER Wildfire 4.0 Basic Design
Introduction Datum features are non-solid features used during the construction of other features. The most common datum features include planes, axes, coordinate systems, and curves. Datum features do
More informationQEM-Filtering: A New Technique For Feature-Sensitive Terrain Mesh Simplification
Volume xx (200y), Number z, pp. 1 5 QEM-Filtering: A New Technique For Feature-Sensitive Terrain Mesh Simplification F. Löffler and H. Schumann University of Rostock / VC 2 G, Germany Abstract Traditionally,
More informationGeometry and Measurement
The student will be able to: Geometry and Measurement 1. Demonstrate an understanding of the principles of geometry and measurement and operations using measurements Use the US system of measurement for
More informationSeminar. Path planning using Voronoi diagrams and B-Splines. Stefano Martina stefano.martina@stud.unifi.it
Seminar Path planning using Voronoi diagrams and B-Splines Stefano Martina stefano.martina@stud.unifi.it 23 may 2016 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International
More informationRendering Microgeometry with Volumetric Precomputed Radiance Transfer
Rendering Microgeometry with Volumetric Precomputed Radiance Transfer John Kloetzli February 14, 2006 Although computer graphics hardware has made tremendous advances over the last few years, there are
More informationGeometry and Topology from Point Cloud Data
Geometry and Topology from Point Cloud Data Tamal K. Dey Department of Computer Science and Engineering The Ohio State University Dey (2011) Geometry and Topology from Point Cloud Data WALCOM 11 1 / 51
More informationReconstruction of Solid Models from Oriented Point Sets
Eurographics Symposium on Geometry Processing (2005) M. Desbrun, H. Pottmann (Editors) Reconstruction of Solid Models from Oriented Point Sets Michael Kazhdan Abstract In this paper we present a novel
More informationAutomatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 269 Class Project Report
Automatic 3D Reconstruction via Object Detection and 3D Transformable Model Matching CS 69 Class Project Report Junhua Mao and Lunbo Xu University of California, Los Angeles mjhustc@ucla.edu and lunbo
More information3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension
3D Model of the City Using LiDAR and Visualization of Flood in Three-Dimension R.Queen Suraajini, Department of Civil Engineering, College of Engineering Guindy, Anna University, India, suraa12@gmail.com
More information3D Distance from a Point to a Triangle
3D Distance from a Point to a Triangle Mark W. Jones Technical Report CSR-5-95 Department of Computer Science, University of Wales Swansea February 1995 Abstract In this technical report, two different
More informationSuch As Statements, Kindergarten Grade 8
Such As Statements, Kindergarten Grade 8 This document contains the such as statements that were included in the review committees final recommendations for revisions to the mathematics Texas Essential
More informationA Fast Scene Constructing Method for 3D Power Big Data Visualization
Journal of Communications Vol. 0, No. 0, October 05 A Fast Scene Constructing Method for 3D Power Big Data Visualization Zhao-Yang Qu and Jing-Yuan Huang School of Information Engineering of Northeast
More information3D Modelling in Blender Based on Polygonal Data
3D Modelling in Blender Based on Polygonal Data James Ribe MSCS Department St. Olaf College 1500 St. Olaf Ave Northfield, MN 55438 ribe@stolaf.edu Alora Killian MSCS Department St. Olaf College 1500 St.
More informationEfficient Next-Best-Scan Planning for Autonomous 3D Surface Reconstruction of Unknown Objects
J Real-Time Image Proc manuscript No. (will be inserted by the editor) Simon Kriegel Christian Rink Tim Bodenmüller Michael Suppa Efficient Next-Best-Scan Planning for Autonomous 3D Surface Reconstruction
More informationGeometry Enduring Understandings Students will understand 1. that all circles are similar.
High School - Circles Essential Questions: 1. Why are geometry and geometric figures relevant and important? 2. How can geometric ideas be communicated using a variety of representations? ******(i.e maps,
More informationRev9 TOPCON Global Marketing Group
Quick Start Guide Rev9 TOPCON Global Marketing Group 1/18 The flow of Data Processing 1-1 Create a new project Obtain data by remote-controlling the GLS-1000 Obtain data by standalone 2-A-1 Connect PC
More informationVisibility Map for Global Illumination in Point Clouds
TIFR-CRCE 2008 Visibility Map for Global Illumination in Point Clouds http://www.cse.iitb.ac.in/ sharat Acknowledgments: Joint work with Rhushabh Goradia. Thanks to ViGIL, CSE dept, and IIT Bombay (Based
More informationEffects of Orientation Disparity Between Haptic and Graphic Displays of Objects in Virtual Environments
Human Computer Interaction INTERACT 99 Angela Sasse and Chris Johnson (Editors) Published by IOS Press, c IFIP TC.13, 1999 1 Effects of Orientation Disparity Between Haptic and Graphic Displays of Objects
More informationGraph/Network Visualization
Graph/Network Visualization Data model: graph structures (relations, knowledge) and networks. Applications: Telecommunication systems, Internet and WWW, Retailers distribution networks knowledge representation
More informationApplication Report. Propeller Blade Inspection Station
34935 SE Douglas Street, Suite, Snoqualmie, WA 9874 Ph: 425-396-5577 Fax: 425-396-586 Application Report Propeller Blade Inspection Station Prepared By Kyle Johnston, Ph. D. Metron Systems Inc.5.5 3 2.5
More informationFour-dimensional Mathematical Data Visualization via Embodied Four-dimensional Space Display System
Original Paper Forma, 26, 11 18, 2011 Four-dimensional Mathematical Data Visualization via Embodied Four-dimensional Space Display System Yukihito Sakai 1,2 and Shuji Hashimoto 3 1 Faculty of Information
More information