Computer Graphics: Programming, Problem Solving, and Visual Communication
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Neural Scene Graph Rendering
Neural Scene Graph Rendering JONATHAN GRANSKOG, NVIDIA TILL N. SCHNABEL, NVIDIA FABRICE ROUSSELLE, NVIDIA JAN NOVÁK, NVIDIA We present a neural scene graph—a modular and controllable representa- · translation Tg,1 (root node) tion of scenes with elements that are learned from data. We focus on the forward rendering problem, where the scene graph is provided by the user and references learned elements. The elements correspond to geometry and material definitions of scene objects and constitute the leaves of thegraph; we store them as high-dimensional vectors. The position and appearance of encoded scene objects can be adjusted in an artist-friendly manner via familiar trans- transformations 3 × 3 formations, e.g. translation, bending, or color hue shift, which are stored in 1 dgg x dgg 2 Tg,21: matrixmatrix Tg,2 4 the inner nodes of the graph. In order to apply a (non-linear) transforma- Tm,1 translation diuse hue tion to a learned vector, we adopt the concept of linearizing a problem by color lifting it into higher dimensions: we first encode the transformation into a shift T1 3g × 3g T1 3m × 3m T2 T3 T3 T4 high-dimensional matrix and then apply it by standard matrix-vector mul- g,3 : matrix m,2 : matrix g,3 g,2 m,1 g,2 tiplication. The transformations are encoded using neural networks. We deformation rotation scaling render the scene graph using a streaming neural renderer, which can handle graphs with a varying number of objects, and thereby facilitates scalability. Our results demonstrate a precise control over the learned object repre- g1 : m1: g2 m2 m3 g4 m4 sentations in a number of animated 2D and 3D scenes. -
Image Processing with the Artificial Swarm Intelligence 86 Xiaodong Zhuang, Nikos E
Advances in Image Analysis - Nature Inspired Methodology Dr. Xiaodong Zhuang 1 Associate Professor, Qingdao University, China 2 WSEAS Research Department, Athens, Greece Prof. Dr. Nikos E. Mastorakis 1 Professor, Technical University of Sofia, Bulgaria 2 Professor, Military Institutes of University Education, Hellenic Naval Academy, Greece 3 WSEAS Headquarters, Athens, Greece Published by WSEAS Press ISBN: 978-960-474-290-5 www.wseas.org Advances in Image Analysis – Nature Inspired Methodology Published by WSEAS Press www.wseas.org Copyright © 2011, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed by two independent reviewers. Acceptance was granted when both reviewers' recommendations were positive. See also: http://www.worldses.org/review/index.html ISBN: 978-960-474-290-5 World Scientific and Engineering Academy and Society Preface The development of human society relies on natural resources in every area (both material and spiritual). Nature has enormous power and intelligence behind its common daily appearance, and it is generous. We learn in it and from it, virtually as part of it. Nature-inspired systems and methods have a long history in human science and technology. For example, in the area of computer science, the recent well-known ones include the artificial neural network, genetic algorithm and swarm intelligence, which solve hard problems by imitating mechanisms in nature. -
Lecture 2 3D Modeling
Lecture 2 3D Modeling Dr. Shuang LIANG School of Software Engineering Tongji University Fall 2012 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Lecturer Dr. Shuang LIANG • Assisstant professor, SSE, Tongji – Education » B.Sc in Computer Science, Zhejiang University, 1999-2003 » PhD in Computer Science, Nanjing Univerisity, 2003-2008 » Visit in Utrecht University, 2007, 2008 – Research Fellowship » The Chinese University of Hong Kong, 2009 » The Hong Kong Polytechnic University, 2010-2011 » The City University of Hong Kong, 2012 • Contact – Office: Room 442, JiShi Building, JiaDing Campus, TongJi (Temporary) – Email: [email protected] 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Outline • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Outline • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 What is a 3D model? 3D object using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, curved surfaces, etc. It is a collection of data (points and other information) 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 What is a 3D modeling? The process of developing a mathematical representation of any three-dimensional -
Matching 3D Models with Shape Distributions
Matching 3D Models with Shape Distributions Robert Osada, Thomas Funkhouser, Bernard Chazelle, and David Dobkin Princeton University Abstract Cad models is a simple example), the vast majority of 3D objects available via the World Wide Web will not have them, and there Measuring the similarity between 3D shapes is a fundamental prob- are few standards regarding their use. In general, 3D models will lem, with applications in computer vision, molecular biology, com- be acquired with scanning devices, or output from geometric ma- puter graphics, and a variety of other fields. A challenging aspect nipulation tools (file format conversion programs), and thus they of this problem is to find a suitable shape signature that can be con- will have only geometric and appearance information, usually com- structed and compared quickly, while still discriminating between pletely void of structure or semantic information. Automatic shape- similar and dissimilar shapes. based matching algorithms will be useful for recognition, retrieval, In this paper, we propose and analyze a method for computing clustering, and classification of 3D models in such databases. shape signatures for arbitrary (possibly degenerate) 3D polygonal Databases of 3D models have several new and interesting charac- models. The key idea is to represent the signature of an object as a teristics that significantly affect shape-based matching algorithms. shape distribution sampled from a shape function measuring global Unlike images and range scans, 3D models do not depend on the geometric properties of an object. The primary motivation for this configuration of cameras, light sources, or surrounding objects approach is to reduce the shape matching problem to the compar- (e.g., mirrors). -
Standard Elevation Models for Evaluating Terrain Representation
Standard elevation models for evaluating terrain representation a, b c d e Patrick Kennelly *, Tom Patterson , Alexander Tait , Bernhard Jenny , Daniel Huffman , Sarah Bell f, Brooke Marston g a Long Island University, [email protected] b US National Parks Service (Ret.), [email protected] c National Geographic Society, [email protected] d Monash University, [email protected] e somethingaboutmaps, [email protected] f Esri, [email protected] g Brooke Marston, [email protected] * Corresponding author Keywords: Terrain representation, digital elevation model, relief shading, standard data models Standard Elevation Models We propose the use of standard elevation models to evaluate and compare the quality of various relief shading and other terrain rendering techniques. These datasets will cover various landforms, be available at no cost to the user, and be free of common data imperfections such as missing data values, resampling artifacts, and seams. Datasets will be available at multiple map scales over the same geographic area for multi-scale analysis. Utilizing a standard data model for testing and comparing methods is a common practice among many disciplines. Furthermore, the use of digital models to test rendering techniques based on qualitative visual production has been well established for decades. Following the generation of the first digital image in 1957, image processing and analysis required standard test images upon which methods could be tested and compared. During the 1960s and 1970s, many well-known standard test images emerged from this need for a comparative technique model. Today, institutions like the University of Southern California’s Signal and Image Processing Institute maintain digital image databases for the primary purpose of supporting image processing and analysis research, including geographic imaging processes. -
Gaboury 10/01/13
Jacob Gaboury 10/01/13 Object Standards, Standard Objects In December 1949 Martin Heidegger gave a series of four lectures in the city of Bremen, then an isolated part of the American occupation zone following the Second World War. The event marked Heidegger’s first speaking engagement following his removal from his Freiburg professorship by the denazification authorities in 1946, and his first public lecture since his foray into university administration and politics in the early 1930s. Titled Insight Into That Which Is [Einblick in das, was ist],1 the lectures mark the debut of a new direction in Heidegger’s thought and introduce a number of major themes that would be explored in his later work.2 Heidegger opened the Bremen lectures with a work simply titled “The Thing” which begins with a meditation on the collapsing of distance, enabled by modern technology. “Physical distance is dissolved by aircraft. The radio makes information instantly available that once went unknown. The formerly slow and mysterious growth of plants is laid bare through stop-action photography.”3 Yet Heidegger argues that despite all conquest of distances the nearness of things remains absent. What about nearness? How can we come to know its nature? Nearness, it seems, cannot be encountered directly. We succeed in reaching it rather by attending to what is near. Near to us are what we usually call things. But what is a thing?4 This question motivates the lecture, and indeed much of Heidegger’s later thought. In 1 Heidegger, Martin. trans. Andrew J. Mitchell. Bremen and Freiburg Lectures: Insight Into That Which Is and Basic Principals of Thinking. -
CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents
1 CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents 1 Administration 5 1.1 General Information . .5 1.2 Topics Covered . .6 1.3 Assignments . .6 2 Introduction 9 2.1 History . .9 2.2 Pipeline . 10 2.3 Primitives . 11 2.4 Algorithms . 12 2.5 APIs . 12 3 Devices and Device Independence 13 3.1 Calligraphic and Raster Devices . 13 3.2 How a Monitor Works . 13 3.3 Physical Devices . 14 4 Device Interfaces 17 4.1 Device Input Modes . 17 4.2 Application Structure . 17 4.3 Polling and Sampling . 18 4.4 Event Queues . 18 4.5 Toolkits and Callbacks . 19 4.6 Example for Discussion . 20 5 Geometries 23 5.1 Vector Spaces . 23 5.2 Affine Spaces . 23 5.3 Inner Product Spaces . 24 5.4 Euclidean Spaces . 24 5.5 Cartesian Space . 25 5.6 Why Vector Spaces Inadequate . 25 5.7 Summary of Geometric Spaces . 27 6 Affine Geometry and Transformations 29 6.1 Linear Combinations . 29 6.2 Affine Combinations . 29 6.3 Affine Transformations . 31 6.4 Matrix Representation of Transformations . 33 6.5 Geometric Transformations . 34 6.6 Compositions of Transformations . 35 6.7 Change of Basis . 36 6.8 Ambiguity . 39 6.9 3D Transformations . 40 CONTENTS 3 6.10 World and Viewing Frames . 42 6.11 Normals . 45 7 Windows, Viewports, NDC 47 7.1 Window to Viewport Mapping . 47 7.2 Normalized Device Coordinates . 49 8 Clipping 51 8.1 Clipping . 51 9 Projections and Projective Transformations 55 9.1 Projections . 55 9.2 Why Map Z? . -
3D Computer Graphics Compiled By: H
animation Charge-coupled device Charts on SO(3) chemistry chirality chromatic aberration chrominance Cinema 4D cinematography CinePaint Circle circumference ClanLib Class of the Titans clean room design Clifford algebra Clip Mapping Clipping (computer graphics) Clipping_(computer_graphics) Cocoa (API) CODE V collinear collision detection color color buffer comic book Comm. ACM Command & Conquer: Tiberian series Commutative operation Compact disc Comparison of Direct3D and OpenGL compiler Compiz complement (set theory) complex analysis complex number complex polygon Component Object Model composite pattern compositing Compression artifacts computationReverse computational Catmull-Clark fluid dynamics computational geometry subdivision Computational_geometry computed surface axial tomography Cel-shaded Computed tomography computer animation Computer Aided Design computerCg andprogramming video games Computer animation computer cluster computer display computer file computer game computer games computer generated image computer graphics Computer hardware Computer History Museum Computer keyboard Computer mouse computer program Computer programming computer science computer software computer storage Computer-aided design Computer-aided design#Capabilities computer-aided manufacturing computer-generated imagery concave cone (solid)language Cone tracing Conjugacy_class#Conjugacy_as_group_action Clipmap COLLADA consortium constraints Comparison Constructive solid geometry of continuous Direct3D function contrast ratioand conversion OpenGL between -
Deep Learning-Based Method for Classifying and Localizing Potato Blemishes
Deep Learning-based Method for Classifying and Localizing Potato Blemishes Sofia Marino, Pierre Beauseroy and André Smolarz Institut Charles Delaunay/M2S, FRE 2019, Université de Technologie de Troyes, Keywords: Deep Learning, Potato Blemishes, Classification, Localization, Autoencoder, SVM. Abstract: In this paper we address the problem of potato blemish classification and localization. A large database with multiple varieties was created containing 6 classes, i.e., healthy, damaged, greening, black dot, common scab and black scurf. A Convolutional Neural Network was trained to classify face potato images and was also used as a filter to select faces where more analysis was required. Then, a combination of autoencoder and SVMs was applied on the selected images to detect damaged and greening defects in a patch-wise manner. The localization results were used to classify the potato according to the severity of the blemish. A final global evaluation of the potato was done where four face images per potato were considered to characterize the entire tuber. Experimental results show a face-wise average precision of 95% and average recall of 93%. For damaged and greening patch-wise localization, we achieve a False Positive Rate of 4.2% and 5.5% and a False Negative Rate of 14.2% and 28.1% respectively. Concerning the final potato-wise classification, we achieved in a test dataset an average precision of 92% and average recall of 91%. 1 INTRODUCTION is the difficulty to design a feature extractor adap- ted to each pattern, that require human expertise to Potato is one of the most important food crops con- suitable transform the raw input image into a good sumed all over the world with a total production that representation, exploitable to achieve the classifica- exceeds 374.000.000 tons (IPC, 2018). -
Dossier [email protected] 0. Introduction (Standards) 1
Dossier [email protected] 0. Introduction (Standards) 1. Recognition 2. Classification 3. Bias & Noise 4. Unknown Known (New Teapots) 0. Introduction (Standards) I’ve been thinking about the problems of standards and categories in computer image recognition, how their abstractions result in hegemonic form that averages and normalizes, and how drawing and modeling might be used to critique and resist these tendencies. The Utah Teapot rendered four ways by Martin Newell To first talk about standards, I’m going to return to some work I did earlier in the semester on the Utah Teapot. The Utah Teapot was created by Martin Newell, a computer graphics researcher in the Graphics Lab at the University of Utah Computer Science Department, in 1975. Newell needed an object for testing 3D scenes, and his wife suggested their Melitta teapot. It was useful to computer graphics researchers primarily because it met certain geometric criteria. “It was round, contained saddle points, had a genus greater than zero because of the hole in the handle, could project a shadow on itself, and could be displayed accurately without a surface texture” (per Wikipedia). But it also was useful because it met some contextual or cultural criteria: it was a familiar, everyday object. The Utah Teapot thus became a standard in the computer graphics world. Original drawing of the Utah Teapot by Martin Newell Outline of the Utah Teapot rotating about the y-axis, Alex Bodkin Outline of the Utah Teapot rotating about the y-axis, Alex Bodkin Outline of the Utah Teapot rotating about -
Classic Models in Computer Graphics • 3D Model Representations • Raw Data • Solids • Surfaces
Lecture 2 3D Modeling Dr. Shuang LIANG School of Software Engineering Tongji University Spring 2013 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 What is a 3D model? 3D object using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, curved surfaces, etc. It is a collection of data (points and other information) 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 What is a 3D modeling? The process of developing a mathematical representation of any three-dimensional surface of object via specialized software. 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Usage of a 3D model The medical industry uses detailed models of organs 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Usage of a 3D model The movie industry uses them as characters -
This Is an Open Access Document Downloaded from ORCA, Cardiff University's Institutional Repository
This is an Open Access document downloaded from ORCA, Cardiff University's institutional repository: http://orca.cf.ac.uk/123147/ This is the author’s version of a work that was submitted to / accepted for publication. Citation for final published version: Song, Ran, Liu, Yonghuai and Rosin, Paul L. 2019. Distinction of 3D objects and scenes via classification network and Markov random field. IEEE Transactions on Visualization and Computer Graphics 10.1109/TVCG.2018.2885750 file Publishers page: http://dx.doi.org/10.1109/TVCG.2018.2885750 <http://dx.doi.org/10.1109/TVCG.2018.2885750> Please note: Changes made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper. This version is being made available in accordance with publisher policies. See http://orca.cf.ac.uk/policies.html for usage policies. Copyright and moral rights for publications made available in ORCA are retained by the copyright holders. MANUSCRIPT SUBMITTED TO TVCG 1 Distinction of 3D Objects and Scenes via Classification Network and Markov Random Field Ran Song, Yonghuai Liu, Senior Member, IEEE and Paul L. Rosin Abstract—An importance measure of 3D objects inspired by human perception has a range of applications since people want computers to behave like humans in many tasks. This paper revisits a well-defined measure, distinction of 3D surface mesh, which indicates how important a region of a mesh is with respect to classification.