Integration of Voxel Colouring Technique in the Volumetric Textures Representation Based on Image Layers
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The Application of Voxel Size Correction in X-Ray Computed Tomography for Dimensional Metrology
SINCE2013 Singapore International NDT Conference & Exhibition 2013, 19-20 July 2013 The Application of Voxel Size Correction in X-ray Computed Tomography for Dimensional Metrology Joseph J. LIFTON1,2, Andrew A. MALCOLM2, John W. MCBRIDE1,3, Kevin J. CROSS1 1The University of Southampton, United Kingdom; Email: [email protected]. 2Singapore Institute of Manufacturing Technology, Singapore; Email: [email protected]. 3The University of Southampton Malaysia Campus (USMC), Malaysia; Email: [email protected]. Abstract X-ray computed tomography (CT) is a non-destructive, radiographic scanning technique that enables the visualisation and dimensional evaluation of both internal and external features of a workpiece; it is therefore an attractive alternative for measurement tasks that prove problematic for conventional tactile and optical instruments. The data output of a CT measurement is a volume of grey value integers that describe the material distribution of the scanned workpiece; the relative spacing of volume-elements (voxels) is termed voxel size and influences all dimensional information evaluated from a CT data-set. Voxel size is defined by the position of a workpiece relative to the X-ray source and detector, and is therefore prone to axis position errors, errors in the geometric alignment of the CT system’s hardware, and the positional drift of the X-ray focal spot. In this work a method is presented for calculating a voxel scaling factor that corrects for voxel size errors, and this method is then applied to a general X-ray CT measurement task and demonstrated to reduce measurement errors. Keywords: X-ray computed tomography, dimensional metrology, calibration, uncertainty, voxel size. -
Temporal Voxel Cone Tracing with Interleaved Sample Patterns by Sanghyeok Hong
c 2015, SangHyeok Hong. All Rights Reserved. The material presented within this document does not necessarily reflect the opinion of the Committee, the Graduate Study Program, or DigiPen Institute of Technology. TEMPORAL VOXEL CONE TRACING WITH INTERLEAVED SAMPLE PATTERNS BY SangHyeok Hong THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science awarded by DigiPen Institute of Technology Redmond, Washington United States of America March 2015 Thesis Advisor: Gary Herron DIGIPEN INSTITUTE OF TECHNOLOGY GRADUATE STUDIES PROGRAM DEFENSE OF THESIS THE UNDERSIGNED VERIFY THAT THE FINAL ORAL DEFENSE OF THE MASTER OF SCIENCE THESIS TITLED Temporal Voxel Cone Tracing with Interleaved Sample Patterns BY SangHyeok Hong HAS BEEN SUCCESSFULLY COMPLETED ON March 12th, 2015. MAJOR FIELD OF STUDY: COMPUTER SCIENCE. APPROVED: Dmitri Volper date Xin Li date Graduate Program Director Dean of Faculty Dmitri Volper date Claude Comair date Department Chair, Computer Science President DIGIPEN INSTITUTE OF TECHNOLOGY GRADUATE STUDIES PROGRAM THESIS APPROVAL DATE: March 12th, 2015 BASED ON THE CANDIDATE'S SUCCESSFUL ORAL DEFENSE, IT IS RECOMMENDED THAT THE THESIS PREPARED BY SangHyeok Hong ENTITLED Temporal Voxel Cone Tracing with Interleaved Sample Patterns BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE AT DIGIPEN INSTITUTE OF TECHNOLOGY. Gary Herron date Xin Li date Thesis Committee Chair Thesis Committee Member Pushpak Karnick date Matt -
Real-Time Rendering Techniques with Hardware Tessellation
Volume 34 (2015), Number x pp. 0–24 COMPUTER GRAPHICS forum Real-time Rendering Techniques with Hardware Tessellation M. Nießner1 and B. Keinert2 and M. Fisher1 and M. Stamminger2 and C. Loop3 and H. Schäfer2 1Stanford University 2University of Erlangen-Nuremberg 3Microsoft Research Abstract Graphics hardware has been progressively optimized to render more triangles with increasingly flexible shading. For highly detailed geometry, interactive applications restricted themselves to performing transforms on fixed geometry, since they could not incur the cost required to generate and transfer smooth or displaced geometry to the GPU at render time. As a result of recent advances in graphics hardware, in particular the GPU tessellation unit, complex geometry can now be generated on-the-fly within the GPU’s rendering pipeline. This has enabled the generation and displacement of smooth parametric surfaces in real-time applications. However, many well- established approaches in offline rendering are not directly transferable due to the limited tessellation patterns or the parallel execution model of the tessellation stage. In this survey, we provide an overview of recent work and challenges in this topic by summarizing, discussing, and comparing methods for the rendering of smooth and highly-detailed surfaces in real-time. 1. Introduction Hardware tessellation has attained widespread use in computer games for displaying highly-detailed, possibly an- Graphics hardware originated with the goal of efficiently imated, objects. In the animation industry, where displaced rendering geometric surfaces. GPUs achieve high perfor- subdivision surfaces are the typical modeling and rendering mance by using a pipeline where large components are per- primitive, hardware tessellation has also been identified as a formed independently and in parallel. -
Human Body Model Acquisition and Tracking Using Voxel Data
Submitted to the International Journal of Computer Vision Human Body Model Acquisition and Tracking using Voxel Data Ivana Mikić2, Mohan Trivedi1, Edward Hunter2, Pamela Cosman1 1Department of Electrical and Computer Engineering University of California, San Diego 2Q3DM, Inc. Abstract We present an integrated system for automatic acquisition of the human body model and motion tracking using input from multiple synchronized video streams. The video frames are segmented and the 3D voxel reconstructions of the human body shape in each frame are computed from the foreground silhouettes. These reconstructions are then used as input to the model acquisition and tracking algorithms. The human body model consists of ellipsoids and cylinders and is described using the twists framework resulting in a non-redundant set of model parameters. Model acquisition starts with a simple body part localization procedure based on template fitting and growing, which uses prior knowledge of average body part shapes and dimensions. The initial model is then refined using a Bayesian network that imposes human body proportions onto the body part size estimates. The tracker is an extended Kalman filter that estimates model parameters based on the measurements made on the labeled voxel data. A voxel labeling procedure that handles large frame-to-frame displacements was designed resulting in the very robust tracking performance. Extensive evaluation shows that the system performs very reliably on sequences that include different types of motion such as walking, sitting, dancing, running and jumping and people of very different body sizes, from a nine year old girl to a tall adult male. 1. Introduction Tracking of the human body, also called motion capture or posture estimation, is a problem of estimating the parameters of the human body model (such as joint angles) from the video data as the position and configuration of the tracked body change over time. -
Photorealistic Scene Reconstruction by Voxel Coloring
In Proc. Computer Vision and Pattern Recognition Conf., pp. 1067-1073, 1997. Photorealistic Scene Reconstruction by Voxel Coloring Steven M. Seitz Charles R. Dyer Department of Computer Sciences University of Wisconsin, Madison, WI 53706 g E-mail: fseitz,dyer @cs.wisc.edu WWW: http://www.cs.wisc.edu/computer-vision Broad Viewpoint Coverage: Reprojections should be Abstract accurate over a large range of target viewpoints. This A novel scene reconstruction technique is presented, requires that the input images are widely distributed different from previous approaches in its ability to cope about the environment with large changes in visibility and its modeling of in- trinsic scene color and texture information. The method The photorealistic scene reconstruction problem, as avoids image correspondence problems by working in a presently formulated, raises a number of unique challenges discretized scene space whose voxels are traversed in a that push the limits of existing reconstruction techniques. fixed visibility ordering. This strategy takes full account Photo integrity requires that the reconstruction be dense of occlusions and allows the input cameras to be far apart and sufficiently accurate to reproduce the original images. and widely distributed about the environment. The algo- This criterion poses a problem for existing feature- and rithm identifies a special set of invariant voxels which to- contour-based techniques that do not provide dense shape gether form a spatial and photometric reconstruction of the estimates. While these techniques can produce texture- scene, fully consistent with the input images. The approach mapped models [1, 3, 4], accuracy is ensured only in places is evaluated with images from both inward- and outward- where features have been detected. -
9.Texture Mapping
OpenGL_PG.book Page 359 Thursday, October 23, 2003 3:23 PM Chapter 9 9.Texture Mapping Chapter Objectives After reading this chapter, you’ll be able to do the following: • Understand what texture mapping can add to your scene • Specify texture images in compressed and uncompressed formats • Control how a texture image is filtered as it’s applied to a fragment • Create and manage texture images in texture objects and, if available, control a high-performance working set of those texture objects • Specify how the color values in the image combine with those of the fragment to which it’s being applied • Supply texture coordinates to indicate how the texture image should be aligned with the objects in your scene • Generate texture coordinates automatically to produce effects such as contour maps and environment maps • Perform complex texture operations in a single pass with multitexturing (sequential texture units) • Use texture combiner functions to mathematically operate on texture, fragment, and constant color values • After texturing, process fragments with secondary colors • Perform transformations on texture coordinates using the texture matrix • Render shadowed objects, using depth textures 359 OpenGL_PG.book Page 360 Thursday, October 23, 2003 3:23 PM So far, every geometric primitive has been drawn as either a solid color or smoothly shaded between the colors at its vertices—that is, they’ve been drawn without texture mapping. If you want to draw a large brick wall without texture mapping, for example, each brick must be drawn as a separate polygon. Without texturing, a large flat wall—which is really a single rectangle—might require thousands of individual bricks, and even then the bricks may appear too smooth and regular to be realistic. -
Online Detector Response Calculations for High-Resolution PET Image Reconstruction
Home Search Collections Journals About Contact us My IOPscience Online detector response calculations for high-resolution PET image reconstruction This article has been downloaded from IOPscience. Please scroll down to see the full text article. 2011 Phys. Med. Biol. 56 4023 (http://iopscience.iop.org/0031-9155/56/13/018) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 171.65.80.217 The article was downloaded on 15/06/2011 at 20:11 Please note that terms and conditions apply. IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY Phys. Med. Biol. 56 (2011) 4023–4040 doi:10.1088/0031-9155/56/13/018 Online detector response calculations for high-resolution PET image reconstruction Guillem Pratx1 and Craig Levin2 1 Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA 2 Departments of Radiology, Physics and Electrical Engineering, and Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA E-mail: [email protected] Received 3 January 2011, in final form 19 May 2011 Published 15 June 2011 Online at stacks.iop.org/PMB/56/4023 Abstract Positron emission tomography systems are best described by a linear shift- varying model. However, image reconstruction often assumes simplified shift- invariant models to the detriment of image quality and quantitative accuracy. We investigated a shift-varying model of the geometrical system response based on an analytical formulation. The model was incorporated within a list- mode, fully 3D iterative reconstruction process in which the system response coefficients are calculated online on a graphics processing unit (GPU). -
Voxel Game Engine Using Blender Game Engine
TALLINN UNIVERSITY OF TECHNOLOGY Faculty of Information Technology Department of Computer Science ITV40LT Fred-Eric Kirsi 135216 IAPB VOXEL GAME ENGINE USING BLENDER GAME ENGINE Bachelor's thesis Supervisor: Jaagup Irve Master of Sciences Software Engineer Tallinn 2016 TALLINNA TEHNIKAÜLIKOOL Infotehnoloogia teaduskond Arvutiteaduse instituut ITV40LT Fred-Eric Kirsi 135216 IAPB VOXEL-MÄNGUMOOTOR BLENDER GAME ENGINE ABIL Bakalaureusetöö Juhendaja: Jaagup Irve Magister Tarkvarainsener Tallinn 2016 Author’s declaration of originality I hereby certify that I am the sole author of this thesis. All the used materials, references to the literature and the work of others have been referred to. This thesis has not been presented for examination anywhere else. Author: Fred-Eric Kirsi 23.05.2016 3 Abstract The objective of this thesis is to study various aspects of developing a voxel style game and in the process create a prototype engine to evaluate the possibilities and performance of the algorithms. The algorithms evaluated are Greedy Meshing, A* search, Goal Oriented Action Planning (GOAP), Finite state machine (FSM), and Perlin noise. The used 3d engine is the Bender game engine. The scripting is done in the Python coding language. The result of this work is a working voxel game prototype, where the agents are able to navigate, manipulate, and make decisions in real time. The aim of the prototype is to quantize the performance and feature experience on for the user. The work is divided into four main stages. Firstly evaluation of the work environment and its limitations. Secondly the development and optimization of the voxel implementation. Thirdly the creation and comparison of different pathing implementations. -
Voxel Map for Visual SLAM
This paper has been accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020. c IEEE Voxel Map for Visual SLAM Manasi Muglikar, Zichao Zhang and Davide Scaramuzza Abstract— In modern visual SLAM systems, it is a stan- Geometry-awareness dard practice to retrieve potential candidate map points from [Newcombe11] overlapping keyframes for further feature matching or direct [Whelan15] Optimal tracking. In this work, we argue that keyframes are not the [Engel14] optimal choice for this task, due to several inherent limitations, Dense representation such as weak geometric reasoning and poor scalability. We propose a voxel-map representation to efficiently retrieve map [Kaess15] This paper points for visual SLAM. In particular, we organize the map [Nardi19] [Rosinol19] points in a regular voxel grid. Visible points from a camera pose Geometric primitives are queried by sampling the camera frustum in a raycasting [Forster17] manner, which can be done in constant time using an efficient [Klein07] [Mur-Artal15] voxel hashing method. Compared with keyframes, the retrieved Sparse keyframes points using our method are geometrically guaranteed to Efficiency fall in the camera field-of-view, and occluded points can be identified and removed to a certain extend. This method also Fig. 1: The optimal SLAM systems should be efficient and have geometrical naturally scales up to large scenes and complicated multi- understanding of the map (denoted by the golden star). Direct methods (red camera configurations. Experimental results show that our dots) associate each keyframe with a semi-dense depth map. They have voxel map representation is as efficient as a keyframe map more scene information but are not efficient. -
Antialiasing
Antialiasing CSE 781 Han-Wei Shen What is alias? Alias - A false signal in telecommunication links from beats between signal frequency and sampling frequency (from dictionary.com) Not just telecommunication, alias is everywhere in computer graphics because rendering is essentially a sampling process Examples: Jagged edges False texture patterns Alias caused by under-sampling 1D signal sampling example Actual signal Sampled signal Alias caused by under-sampling 2D texture display example Minor aliasing worse aliasing How often is enough? What is the right sampling frequency? Sampling theorem (or Nyquist limit) - the sampling frequency has to be at least twice the maximum frequency of the signal to be sampled Need two samples in this cycle Reconstruction After the (ideal) sampling is done, we need to reconstruct back the original continuous signal The reconstruction is done by reconstruction filter Reconstruction Filters Common reconstruction filters: Box filter Tent filter Sinc filter = sin(πx)/πx Anti-aliased texture mapping Two problems to address – Magnification Minification Re-sampling Minification and Magnification – resample the signal to a different resolution Minification Magnification (note the minification is done badly here) Magnification Simpler to handle, just resample the reconstructed signal Reconstructed signal Resample the reconstructed signal Magnification Common methods: nearest neighbor (box filter) or linear interpolation (tent filter) Nearest neighbor bilinear interpolation Minification Harder to handle The signal’s frequency is too high to avoid aliasing A possible solution: Increase the low pass filter width of the ideal sinc filter – this effectively blur the image Blur the image first (using any method), and then sample it Minification Several texels cover one pixel (under sampling happens) Solution: Either increase sampling rate or reduce the texture Frequency one pixel We will discuss: Under sample artifact 1. -
Future Graphics in Games
Future graphics in games Anton Kaplanyan Lead researcher Cevat Yerli Crytek CEO Agenda • The history: Crytek GmbH • Current graphics technologies • Stereoscopic rendering • Current graphics challenges • Graphics of the future • Graphics technologies of the future • Server-side rendering • Hardware challenges • Perception-driven graphics The Past - Part 1 • March 2001 till March 2004 • Development of Far Cry • Development of CryEngine 1 • Approach: A naïve, but successful push for contrasts, by insisting on opposites to industry. size, quality, detail, brightness • First right investment into tools - WYSIWYPlay Past - Part 1: CryEngine 1 • Polybump (2001) • NormalMap extraction from High-Res Geometry • First „Per Pixel Shading“ & HDR Engine • For Lights, Shadows & Materials • High Dynamic Range • Long view distances & detailed vistas • Terrain featured unique base-texturing • High quality close ranges • High fidelity physics & AI • It took 3 years, avg 20 R&D Engineers CryENGINE 2 The Past – Part 2 – CryENGINE 2 • April 2004 till November 2007 • Development of Crysis • Development of CryEngine 2 • Approach: Photorealism meets interactivity! • Typically mutual exclusive directions • Realtime productivity with WYSIWYPlay • Extremely challenging, but successful CryEngine 2 - Way to Photorealism The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. The Past - Part 2: CryEngine 2 • CGI Quality Lighting & Shading • Life-like characters • Scaleable architecture in • Both content and pipeline • Technologies and assets allow various configurations to be maxed out! • Crysis shipped Nov 2007, works on PCs of 2004 till today and for future.. -
GPU Volume Voxelization Exploration of the Performance Characteristics of Different GPU-Based Implementations
EXAMENSARBETE INOM TEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2019 GPU Volume Voxelization Exploration of the performance characteristics of different GPU-based implementations GRIGORY GLUKHOV ALEKSANDRA SOLTAN KTH SKOLAN FÖR ELEKTROTEKNIK OCH DATAVETENSKAP GPU Volume Voxelization Exploration of the performance characteris- tics of different GPU-based implementations Aleksandra Soltan Grigory Glukhov Examiner Johan Montelius Supervisor Thomas Sjöland A thesis presented for the degree of Bachelor of Information and Communication Technology KTH Royal Institute of Technology School of Electrical Engineering and Computer Science SE-100 44 Stockholm, Sweden June 2019 page intentionally left blank 1 Abstract In recent years, voxel-based modelling has seen a reintroduction to computer game development through massive graphics hardware improvements. Never- theless, polygons continue to be the default building block of 3D objects, intro- ducing a need for the transformation of polygon meshes into voxel-based models; this process is known as voxelization. Efficient voxelization algorithms take ad- vantage of the flexibility and control offered by modern, programmable GPU pipelines. However, the variability in possible approaches poses the question of how different GPU-based implementations affect voxelization performance. This thesis explores the impact of GPU-based improvements by comparing four different implementations of a solid voxelization algorithm. The implemen- tations include a naive transition from the CPU to the GPU, a non-branching execution path approach, data pre-processing, and a combination of the two previous approaches. Benchmarking experiments run on four, standard polygo- nal models and three graphics cards (NVIDIA and AMD) provide runtime and memory usage data for each implementation. A comparative analysis is per- formed on the basis of this data to determine the performance impact of the GPU-based adjustments to the voxelization algorithm implementation.