3D Computer Vision 1
|
|
- June James
- 5 years ago
- Views:
Transcription
1 3D Computer Vision 1
2 Multiview Stereo
3 Multiview Stereo
4 Multiview Stereo
5 Shape from silhouette
6 Shape from silhouette
7 Shape from silhouette
8 Shape from silhouette
9 Structured light
10 Kinect: Structured infrared light
11 Photometry stereo Helmholtz Stereopsis
12 3D Imaging with ToF Camera
13 Time-of-Flight Principle Time-of-flight of Light Distance : It is not simple to measure the flight time directly at each pixel of any existing image sensor Reflected IR shows phase delay proportional to the distance from the camera.
14 Phase Delay Measurement Q1 through Q4 are the amount of electrons measured at each corresponding time. t(d In real situations, it is difficult to sense electric charge at certain time instance
15 Phase Delay Measurement Distance arctan 2 ( 2 Q Q Q Q c d t c arctan 2 arctan 2 c c Assumption: Single reflected IR signal In principle, amplitude of the reflected IR does not affect the depth calculation.
16 Multiple IR Signals - Large Sensor Pixel - Scattering - Multipath - Motion Blur - Transparent Object In real situations, multiple reflected IR signals with different phase delays & amplitudes can be superposed. ( ( ( ( arctan 2 ( c d t We do not know how many IR signals will be superposed.
17 Large Sensor Pixel In order to increase sensitivity, - large pixel size or pixel binning IR signal #1 IR signal #2 ( ( ( ( arctan 2 ( c d t
18 Light Scattering Multiple light reflections between the lens and the sensor Light scattering [1] [1] Real-time scattering compensation for time-of-flight camera, CVS07
19 Multipath Errors IR LED Sensor Multipath Interference Depth error in concave objects ( ( ( ( arctan 2 ( c d t
20 Motion Blur Moving camera/object within single integration time make wrong depth calculation Moving Object Moving Object Image sensor
21 Motion Blur The characteristic of Tof motion blur is different from color Overshoot Blur Undershoot Blur Overshoot Blur
22 Transparent Object 2-Layer approximation of transparent object (1 ( (1 ( (1 ( (1 ( arctan 2 ( c d t - Sometimes 2-Layer is not enough - Multiple reflection between objects (when they are close - In most cases, they have specular surface
23 Integration time-related Error Due to the variation of the number of collected electrons during the integration time the repeatability of each depth point varies Integration Time: 30(ms Integration Time: 80(ms
24 y(pixel Error(m Amplitude-related Errors Due to the non-uniformity of IR illumination and reflectivity variation of objects use a polynomial fitting model x(pixel Amplitude image of a planar object with a ramp image. Parts of the ramp are selected for calibration (blue rectangle Amplitude The depth samples (blue and the fitted model (green to the error 1
25 Kinect Principle (1/3 Basically, it is based on structured light principle IR Speckle Pattern
26 Kinect Principle (2/3 0. Calibrate source and detector 1. Known IR pattern is projected from the source 2. Detector identify each dot (or set of dots 3. Triangulate to calculate depth
27 Kinect Principle (3/3 - Random speckles identify x,y locations - Orientation and shape of the speckles change along distance identify z location
28 Depth/Point Cloud Processing 3D Features 3D Filtering Registration Surface Processing 28
29 Depth Distortion Upon Materials Conventional approaches assume the Lambertian materials. Various surface materials exhibit the complex light interaction, causing the non-linear distortion on light transport. Depth cameras suffer from the depth distortion upon material properties. The type of distortion varies upon the sensing principle of depth cameras. 29
30 Depth Cameras We provide the distortion analysis based on two sensor types: A Time-of-Flight and a structured light sensor [Swissranger] [Kinect] 30
31 Depth Distortion Lambertian Material affects the sensing performance (Lambertian ToF depth camera Structured light depth camera IR LED Projector Sensor All existing 3D sensing techniues are limited to Lambertian object. Sensor 31
32 Depth Distortion Specularity Non-Lambertian materials causes the failure in sensing reflected signal (Specularity ToF depth camera Structured light depth camera IR LED Projector Sensor Sensor 32
33 Depth Distortion Translucency Non-Lambertian materials causes the failure in sensing reflected signal (Translucency ToF depth camera Structured light depth camera IR LED IR LED Projector Sensor Sensor 33
34 Depth Distortion Global Illumination Complex illumination affects the sensing performance (Global Illumination ToF depth camera Structured light depth camera IR LED IR LED Projector Sensor Sensor 34
35 Color-Depth Calibration
36 Given a calibrated TOF-Stereo system Each TOF point P T defines a correspondence between P L and P R
37 Correspondences (samples obtained by using the calibration parameters each correspondence comes from a TOF point different color -> different depth
38 Correspondences (samples obtained by using the calibration parameters each correspondence comes from a TOF point different color -> different depth
39 TOF-to-Left Mapping We use the left image as reference
40 TOF-to-Left Mapping Resolution mismatch
41 TOF-to-Left Mapping Left-to-Tof Occlusions Left-to-Tof Occlusions: the depth decreases from left to right
42 TOF-to-Left Mapping Tof-to-Left Occlusions Tof-to-Left Occlusions: the depth increases from left to right
43 Paper List 1 [Depth from Stereo] DeMoN: Depth and Motion Network for Learning Monocular Stereo Ummenhofer et al. CVPR [RGBD for object recog.] Multimodal Deep Learning for Robust RGB-D Object Recognition Eitel et al. IROS [Multiview 3D] RayNet : Learning Volumetric 3D Reconstruction with Ray Potential Paschalidou et al. CVPR [Multiview 3D] 3D-R2N2: A Unifed Approach for Single and Multi-view 3D Object Reconstruction Choy et al. ECCV
Depth Sensors Kinect V2 A. Fornaser
Depth Sensors Kinect V2 A. Fornaser alberto.fornaser@unitn.it Vision Depth data It is not a 3D data, It is a map of distances Not a 3D, not a 2D it is a 2.5D or Perspective 3D Complete 3D - Tomography
More informationLecture 19: Depth Cameras. Visual Computing Systems CMU , Fall 2013
Lecture 19: Depth Cameras Visual Computing Systems Continuing theme: computational photography Cameras capture light, then extensive processing produces the desired image Today: - Capturing scene depth
More information3D Computer Vision. Depth Cameras. Prof. Didier Stricker. Oliver Wasenmüller
3D Computer Vision Depth Cameras Prof. Didier Stricker Oliver Wasenmüller Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de
More information3D Photography: Active Ranging, Structured Light, ICP
3D Photography: Active Ranging, Structured Light, ICP Kalin Kolev, Marc Pollefeys Spring 2013 http://cvg.ethz.ch/teaching/2013spring/3dphoto/ Schedule (tentative) Feb 18 Feb 25 Mar 4 Mar 11 Mar 18 Mar
More information3D Photography: Stereo
3D Photography: Stereo Marc Pollefeys, Torsten Sattler Spring 2016 http://www.cvg.ethz.ch/teaching/3dvision/ 3D Modeling with Depth Sensors Today s class Obtaining depth maps / range images unstructured
More informationTime of Flight Cameras: Principles, Methods, and Applications
Time of Flight Cameras: Principles, Methods, and Applications Miles Hansard, Seungkyu Lee, Ouk Choi, Radu Horaud To cite this version: Miles Hansard, Seungkyu Lee, Ouk Choi, Radu Horaud. Time of Flight
More informationL2 Data Acquisition. Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods
L2 Data Acquisition Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods 1 Coordinate Measurement Machine Touch based Slow Sparse Data Complex planning Accurate 2
More informationOther Reconstruction Techniques
Other Reconstruction Techniques Ruigang Yang CS 684 CS 684 Spring 2004 1 Taxonomy of Range Sensing From Brain Curless, SIGGRAPH 00 Lecture notes CS 684 Spring 2004 2 Taxonomy of Range Scanning (cont.)
More informationMulti-view Stereo. Ivo Boyadzhiev CS7670: September 13, 2011
Multi-view Stereo Ivo Boyadzhiev CS7670: September 13, 2011 What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape
More information3D object recognition used by team robotto
3D object recognition used by team robotto Workshop Juliane Hoebel February 1, 2016 Faculty of Computer Science, Otto-von-Guericke University Magdeburg Content 1. Introduction 2. Depth sensor 3. 3D object
More informationA Comparison between Active and Passive 3D Vision Sensors: BumblebeeXB3 and Microsoft Kinect
A Comparison between Active and Passive 3D Vision Sensors: BumblebeeXB3 and Microsoft Kinect Diana Beltran and Luis Basañez Technical University of Catalonia, Barcelona, Spain {diana.beltran,luis.basanez}@upc.edu
More informationThe main problem of photogrammetry
Structured Light Structured Light The main problem of photogrammetry to recover shape from multiple views of a scene, we need to find correspondences between the images the matching/correspondence problem
More informationVisual Perception Sensors
G. Glaser Visual Perception Sensors 1 / 27 MIN Faculty Department of Informatics Visual Perception Sensors Depth Determination Gerrit Glaser University of Hamburg Faculty of Mathematics, Informatics and
More informationOutline. ETN-FPI Training School on Plenoptic Sensing
Outline Introduction Part I: Basics of Mathematical Optimization Linear Least Squares Nonlinear Optimization Part II: Basics of Computer Vision Camera Model Multi-Camera Model Multi-Camera Calibration
More informationTime-of-Flight Imaging!
Time-of-Flight Imaging Loren Schwarz, Nassir Navab 3D Computer Vision II Winter Term 2010 21.12.2010 Lecture Outline 1. Introduction and Motivation 2. Principles of ToF Imaging 3. Computer Vision with
More informationImage Based Reconstruction II
Image Based Reconstruction II Qixing Huang Feb. 2 th 2017 Slide Credit: Yasutaka Furukawa Image-Based Geometry Reconstruction Pipeline Last Lecture: Multi-View SFM Multi-View SFM This Lecture: Multi-View
More informationIndex C, D, E, F I, J
Index A Ambient light, 12 B Blurring algorithm, 68 Brightness thresholding algorithm float testapp::blur, 70 kinect.update(), 69 void testapp::draw(), 70 void testapp::exit(), 70 void testapp::setup(),
More informationNew Sony DepthSense TM ToF Technology
ADVANCED MATERIAL HANDLING WITH New Sony DepthSense TM ToF Technology Jenson Chang Product Marketing November 7, 2018 1 3D SENSING APPLICATIONS Pick and Place Drones Collision Detection People Counting
More informationNew Sony DepthSense TM ToF Technology
ADVANCED MATERIAL HANDLING WITH New Sony DepthSense TM ToF Technology Jenson Chang Product Marketing November 7, 2018 1 3D SENSING APPLICATIONS Pick and Place Drones Collision Detection People Counting
More informationMulti-View 3D-Reconstruction
Multi-View 3D-Reconstruction Cedric Cagniart Computer Aided Medical Procedures (CAMP) Technische Universität München, Germany 1 Problem Statement Given several calibrated views of an object... can we automatically
More informationHigh-Fidelity Augmented Reality Interactions Hrvoje Benko Researcher, MSR Redmond
High-Fidelity Augmented Reality Interactions Hrvoje Benko Researcher, MSR Redmond New generation of interfaces Instead of interacting through indirect input devices (mice and keyboard), the user is interacting
More informationLecture 8 Active stereo & Volumetric stereo
Lecture 8 Active stereo & Volumetric stereo Active stereo Structured lighting Depth sensing Volumetric stereo: Space carving Shadow carving Voxel coloring Reading: [Szelisky] Chapter 11 Multi-view stereo
More informationComputer and Machine Vision
Computer and Machine Vision Lecture Week 12 Part-2 Additional 3D Scene Considerations March 29, 2014 Sam Siewert Outline of Week 12 Computer Vision APIs and Languages Alternatives to C++ and OpenCV API
More information3D Time-of-Flight Image Sensor Solutions for Mobile Devices
3D Time-of-Flight Image Sensor Solutions for Mobile Devices SEMICON Europa 2015 Imaging Conference Bernd Buxbaum 2015 pmdtechnologies gmbh c o n f i d e n t i a l Content Introduction Motivation for 3D
More informationStereo and structured light
Stereo and structured light http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 20 Course announcements Homework 5 is still ongoing. - Make sure
More informationActive Stereo Vision. COMP 4900D Winter 2012 Gerhard Roth
Active Stereo Vision COMP 4900D Winter 2012 Gerhard Roth Why active sensors? Project our own texture using light (usually laser) This simplifies correspondence problem (much easier) Pluses Can handle different
More informationCS4495/6495 Introduction to Computer Vision
CS4495/6495 Introduction to Computer Vision 9C-L1 3D perception Some slides by Kelsey Hawkins Motivation Why do animals, people & robots need vision? To detect and recognize objects/landmarks Is that a
More informationHuman Body Recognition and Tracking: How the Kinect Works. Kinect RGB-D Camera. What the Kinect Does. How Kinect Works: Overview
Human Body Recognition and Tracking: How the Kinect Works Kinect RGB-D Camera Microsoft Kinect (Nov. 2010) Color video camera + laser-projected IR dot pattern + IR camera $120 (April 2012) Kinect 1.5 due
More information3D Scanning. Qixing Huang Feb. 9 th Slide Credit: Yasutaka Furukawa
3D Scanning Qixing Huang Feb. 9 th 2017 Slide Credit: Yasutaka Furukawa Geometry Reconstruction Pipeline This Lecture Depth Sensing ICP for Pair-wise Alignment Next Lecture Global Alignment Pairwise Multiple
More informationThree-Dimensional Sensors Lecture 2: Projected-Light Depth Cameras
Three-Dimensional Sensors Lecture 2: Projected-Light Depth Cameras Radu Horaud INRIA Grenoble Rhone-Alpes, France Radu.Horaud@inria.fr http://perception.inrialpes.fr/ Outline The geometry of active stereo.
More informationNon-line-of-sight imaging
Non-line-of-sight imaging http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 25 Course announcements Homework 6 will be posted tonight. - Will
More informationDense 3D Reconstruction. Christiano Gava
Dense 3D Reconstruction Christiano Gava christiano.gava@dfki.de Outline Previous lecture: structure and motion II Structure and motion loop Triangulation Wide baseline matching (SIFT) Today: dense 3D reconstruction
More informationThere are many cues in monocular vision which suggests that vision in stereo starts very early from two similar 2D images. Lets see a few...
STEREO VISION The slides are from several sources through James Hays (Brown); Srinivasa Narasimhan (CMU); Silvio Savarese (U. of Michigan); Bill Freeman and Antonio Torralba (MIT), including their own
More informationOther approaches to obtaining 3D structure
Other approaches to obtaining 3D structure Active stereo with structured light Project structured light patterns onto the object simplifies the correspondence problem Allows us to use only one camera camera
More informationDense 3D Reconstruction. Christiano Gava
Dense 3D Reconstruction Christiano Gava christiano.gava@dfki.de Outline Previous lecture: structure and motion II Structure and motion loop Triangulation Today: dense 3D reconstruction The matching problem
More informationBinocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?
Binocular Stereo Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Where does the depth information come from? Binocular stereo Given a calibrated binocular stereo
More informationToF Camera for high resolution 3D images with affordable pricing
ToF Camera for high resolution 3D images with affordable pricing Basler AG Jana Bartels, Product Manager 3D Agenda Coming next I. Basler AG II. 3D Purpose and Time-of-Flight - Working Principle III. Advantages
More informationDepth Camera for Mobile Devices
Depth Camera for Mobile Devices Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Stereo Cameras Structured Light Cameras Time of Flight (ToF) Camera Inferring 3D Points Given we have
More informationComputer Vision Lecture 17
Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics 13.01.2015 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Announcements Seminar in the summer semester
More informationInline Computational Imaging: Single Sensor Technology for Simultaneous 2D/3D High Definition Inline Inspection
Inline Computational Imaging: Single Sensor Technology for Simultaneous 2D/3D High Definition Inline Inspection Svorad Štolc et al. svorad.stolc@ait.ac.at AIT Austrian Institute of Technology GmbH Center
More informationComputer Vision Lecture 17
Announcements Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics Seminar in the summer semester Current Topics in Computer Vision and Machine Learning Block seminar, presentations in 1 st week
More informationMulti-view stereo. Many slides adapted from S. Seitz
Multi-view stereo Many slides adapted from S. Seitz Beyond two-view stereo The third eye can be used for verification Multiple-baseline stereo Pick a reference image, and slide the corresponding window
More informationMultiple View Geometry
Multiple View Geometry Martin Quinn with a lot of slides stolen from Steve Seitz and Jianbo Shi 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 Our Goal The Plenoptic Function P(θ,φ,λ,t,V
More informationThe Kinect Sensor. Luís Carriço FCUL 2014/15
Advanced Interaction Techniques The Kinect Sensor Luís Carriço FCUL 2014/15 Sources: MS Kinect for Xbox 360 John C. Tang. Using Kinect to explore NUI, Ms Research, From Stanford CS247 Shotton et al. Real-Time
More informationIntroduction to 3D Machine Vision
Introduction to 3D Machine Vision 1 Many methods for 3D machine vision Use Triangulation (Geometry) to Determine the Depth of an Object By Different Methods: Single Line Laser Scan Stereo Triangulation
More information3D Object Representations. COS 526, Fall 2016 Princeton University
3D Object Representations COS 526, Fall 2016 Princeton University 3D Object Representations How do we... Represent 3D objects in a computer? Acquire computer representations of 3D objects? Manipulate computer
More informationSurround Structured Lighting for Full Object Scanning
Surround Structured Lighting for Full Object Scanning Douglas Lanman, Daniel Crispell, and Gabriel Taubin Brown University, Dept. of Engineering August 21, 2007 1 Outline Introduction and Related Work
More informationEpipolar geometry contd.
Epipolar geometry contd. Estimating F 8-point algorithm The fundamental matrix F is defined by x' T Fx = 0 for any pair of matches x and x in two images. Let x=(u,v,1) T and x =(u,v,1) T, each match gives
More informationProject Updates Short lecture Volumetric Modeling +2 papers
Volumetric Modeling Schedule (tentative) Feb 20 Feb 27 Mar 5 Introduction Lecture: Geometry, Camera Model, Calibration Lecture: Features, Tracking/Matching Mar 12 Mar 19 Mar 26 Apr 2 Apr 9 Apr 16 Apr 23
More informationAdvanced Vision Guided Robotics. David Bruce Engineering Manager FANUC America Corporation
Advanced Vision Guided Robotics David Bruce Engineering Manager FANUC America Corporation Traditional Vision vs. Vision based Robot Guidance Traditional Machine Vision Determine if a product passes or
More informationComputational Imaging for Self-Driving Vehicles
CVPR 2018 Computational Imaging for Self-Driving Vehicles Jan Kautz--------Ramesh Raskar--------Achuta Kadambi--------Guy Satat Computational Imaging for Self-Driving Vehicles Jan Kautz--------Ramesh Raskar--------Achuta
More informationOutdoor Scene Reconstruction from Multiple Image Sequences Captured by a Hand-held Video Camera
Outdoor Scene Reconstruction from Multiple Image Sequences Captured by a Hand-held Video Camera Tomokazu Sato, Masayuki Kanbara and Naokazu Yokoya Graduate School of Information Science, Nara Institute
More informationProject 2 due today Project 3 out today. Readings Szeliski, Chapter 10 (through 10.5)
Announcements Stereo Project 2 due today Project 3 out today Single image stereogram, by Niklas Een Readings Szeliski, Chapter 10 (through 10.5) Public Library, Stereoscopic Looking Room, Chicago, by Phillips,
More informationTime-of-flight basics
Contents 1. Introduction... 2 2. Glossary of Terms... 3 3. Recovering phase from cross-correlation... 4 4. Time-of-flight operating principle: the lock-in amplifier... 6 5. The time-of-flight sensor pixel...
More informationCapturing light. Source: A. Efros
Capturing light Source: A. Efros Review Pinhole projection models What are vanishing points and vanishing lines? What is orthographic projection? How can we approximate orthographic projection? Lenses
More informationStereo. 11/02/2012 CS129, Brown James Hays. Slides by Kristen Grauman
Stereo 11/02/2012 CS129, Brown James Hays Slides by Kristen Grauman Multiple views Multi-view geometry, matching, invariant features, stereo vision Lowe Hartley and Zisserman Why multiple views? Structure
More informationAn Evaluation of Volumetric Interest Points
An Evaluation of Volumetric Interest Points Tsz-Ho YU Oliver WOODFORD Roberto CIPOLLA Machine Intelligence Lab Department of Engineering, University of Cambridge About this project We conducted the first
More information3D Computer Vision. Dense 3D Reconstruction II. Prof. Didier Stricker. Christiano Gava
3D Computer Vision Dense 3D Reconstruction II Prof. Didier Stricker Christiano Gava Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de
More informationEECS 442 Computer vision. Announcements
EECS 442 Computer vision Announcements Midterm released after class (at 5pm) You ll have 46 hours to solve it. it s take home; you can use your notes and the books no internet must work on it individually
More informationStereo vision. Many slides adapted from Steve Seitz
Stereo vision Many slides adapted from Steve Seitz What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape What is
More informationCS4670/5760: Computer Vision Kavita Bala Scott Wehrwein. Lecture 23: Photometric Stereo
CS4670/5760: Computer Vision Kavita Bala Scott Wehrwein Lecture 23: Photometric Stereo Announcements PA3 Artifact due tonight PA3 Demos Thursday Signups close at 4:30 today No lecture on Friday Last Time:
More informationCS5670: Computer Vision
CS5670: Computer Vision Noah Snavely Light & Perception Announcements Quiz on Tuesday Project 3 code due Monday, April 17, by 11:59pm artifact due Wednesday, April 19, by 11:59pm Can we determine shape
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 14 130307 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Stereo Dense Motion Estimation Translational
More informationUnderstanding Variability
Understanding Variability Why so different? Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic aberration, radial distortion
More informationSurround Structured Lighting for Full Object Scanning
Surround Structured Lighting for Full Object Scanning Douglas Lanman, Daniel Crispell, and Gabriel Taubin Department of Engineering, Brown University {dlanman,daniel crispell,taubin}@brown.edu Abstract
More informationStereo. Many slides adapted from Steve Seitz
Stereo Many slides adapted from Steve Seitz Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image image 1 image 2 Dense depth map Binocular stereo Given a calibrated
More informationStructured Light. Tobias Nöll Thanks to Marc Pollefeys, David Nister and David Lowe
Structured Light Tobias Nöll tobias.noell@dfki.de Thanks to Marc Pollefeys, David Nister and David Lowe Introduction Previous lecture: Dense reconstruction Dense matching of non-feature pixels Patch-based
More informationLecture 8 Active stereo & Volumetric stereo
Lecture 8 Active stereo & Volumetric stereo In this lecture, we ll first discuss another framework for describing stereo systems called active stereo, and then introduce the problem of volumetric stereo,
More informationImplementation of 3D Object Reconstruction Using Multiple Kinect Cameras
Implementation of 3D Object Reconstruction Using Multiple Kinect Cameras Dong-Won Shin and Yo-Sung Ho; Gwangju Institute of Science of Technology (GIST); Gwangju, Republic of Korea Abstract Three-dimensional
More informationInformation page for written examinations at Linköping University TER2
Information page for written examinations at Linköping University Examination date 2016-08-19 Room (1) TER2 Time 8-12 Course code Exam code Course name Exam name Department Number of questions in the examination
More information3D Sensing. 3D Shape from X. Perspective Geometry. Camera Model. Camera Calibration. General Stereo Triangulation.
3D Sensing 3D Shape from X Perspective Geometry Camera Model Camera Calibration General Stereo Triangulation 3D Reconstruction 3D Shape from X shading silhouette texture stereo light striping motion mainly
More information3D scanning. 3D scanning is a family of technologies created as a means of automatic measurement of geometric properties of objects.
Acquiring 3D shape 3D scanning 3D scanning is a family of technologies created as a means of automatic measurement of geometric properties of objects. The produced digital model is formed by geometric
More informationDense Tracking and Mapping for Autonomous Quadrocopters. Jürgen Sturm
Computer Vision Group Prof. Daniel Cremers Dense Tracking and Mapping for Autonomous Quadrocopters Jürgen Sturm Joint work with Frank Steinbrücker, Jakob Engel, Christian Kerl, Erik Bylow, and Daniel Cremers
More informationInternational Conference on Communication, Media, Technology and Design. ICCMTD May 2012 Istanbul - Turkey
VISUALIZING TIME COHERENT THREE-DIMENSIONAL CONTENT USING ONE OR MORE MICROSOFT KINECT CAMERAS Naveed Ahmed University of Sharjah Sharjah, United Arab Emirates Abstract Visualizing or digitization of the
More information3D Photography: Stereo Matching
3D Photography: Stereo Matching Kevin Köser, Marc Pollefeys Spring 2012 http://cvg.ethz.ch/teaching/2012spring/3dphoto/ Stereo & Multi-View Stereo Tsukuba dataset http://cat.middlebury.edu/stereo/ Stereo
More informationA Unified Approach to Calibrate a Network of Camcorders and ToF cameras
A Unified Approach to Calibrate a Network of Camcorders and ToF cameras Li Guan lguan@cs.unc.edu Marc Pollefeys marc.pollefeys@inf.ethz.ch UNC-Chapel Hill, USA. ETH-Zürich, Switzerland. Abstract. In this
More informationStructured light 3D reconstruction
Structured light 3D reconstruction Reconstruction pipeline and industrial applications rodola@dsi.unive.it 11/05/2010 3D Reconstruction 3D reconstruction is the process of capturing the shape and appearance
More information: Easy 3D Calibration of laser triangulation systems. Fredrik Nilsson Product Manager, SICK, BU Vision
: Easy 3D Calibration of laser triangulation systems Fredrik Nilsson Product Manager, SICK, BU Vision Using 3D for Machine Vision solutions : 3D imaging is becoming more important and well accepted for
More informationEECS 442 Computer vision. Stereo systems. Stereo vision Rectification Correspondence problem Active stereo vision systems
EECS 442 Computer vision Stereo systems Stereo vision Rectification Correspondence problem Active stereo vision systems Reading: [HZ] Chapter: 11 [FP] Chapter: 11 Stereo vision P p p O 1 O 2 Goal: estimate
More informationExploring the Potential of Combining Time of Flight and Thermal Infrared Cameras for Person Detection
Exploring the Potential of Combining Time of Flight and Thermal Infrared Cameras for Person Detection Wim Abbeloos and Toon Goedemé KU Leuven, Department of Electrical Engineering, EAVISE Leuven, Belgium
More information3D Pose Estimation and Mapping with Time-of-Flight Cameras. S. May 1, D. Dröschel 1, D. Holz 1, C. Wiesen 1 and S. Fuchs 2
3D Pose Estimation and Mapping with Time-of-Flight Cameras S. May 1, D. Dröschel 1, D. Holz 1, C. Wiesen 1 and S. Fuchs 2 1 2 Objectives 3D Pose Estimation and Mapping Based on ToF camera data No additional
More informationSrikumar Ramalingam. Review. 3D Reconstruction. Pose Estimation Revisited. School of Computing University of Utah
School of Computing University of Utah Presentation Outline 1 2 3 Forward Projection (Reminder) u v 1 KR ( I t ) X m Y m Z m 1 Backward Projection (Reminder) Q K 1 q Q K 1 u v 1 What is pose estimation?
More informationNon-line-of-sight imaging
Non-line-of-sight imaging http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 23 Course announcements Homework 6 posted, due November 30 th. -
More information3D Shape and Indirect Appearance By Structured Light Transport
3D Shape and Indirect Appearance By Structured Light Transport CVPR 2014 - Best paper honorable mention Matthew O Toole, John Mather, Kiriakos N. Kutulakos Department of Computer Science University of
More informationReview on Feature Detection and Matching Algorithms for 3D Object Reconstruction
Review on Feature Detection and Matching Algorithms for 3D Object Reconstruction Amit Banda 1,Rajesh Patil 2 1 M. Tech Scholar, 2 Associate Professor Electrical Engineering Dept.VJTI, Mumbai, India Abstract
More informationStereo Vision II: Dense Stereo Matching
Stereo Vision II: Dense Stereo Matching Nassir Navab Slides prepared by Christian Unger Outline. Hardware. Challenges. Taxonomy of Stereo Matching. Analysis of Different Problems. Practical Considerations.
More informationReal-Time Human Detection using Relational Depth Similarity Features
Real-Time Human Detection using Relational Depth Similarity Features Sho Ikemura, Hironobu Fujiyoshi Dept. of Computer Science, Chubu University. Matsumoto 1200, Kasugai, Aichi, 487-8501 Japan. si@vision.cs.chubu.ac.jp,
More informationLigh%ng and Reflectance
Ligh%ng and Reflectance 2 3 4 Ligh%ng Ligh%ng can have a big effect on how an object looks. Modeling the effect of ligh%ng can be used for: Recogni%on par%cularly face recogni%on Shape reconstruc%on Mo%on
More informationVolumetric stereo with silhouette and feature constraints
Volumetric stereo with silhouette and feature constraints Jonathan Starck, Gregor Miller and Adrian Hilton Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK.
More informationStructured light , , Computational Photography Fall 2017, Lecture 27
Structured light http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 27 Course announcements Homework 5 has been graded. - Mean: 129. - Median:
More informationKinect Device. How the Kinect Works. Kinect Device. What the Kinect does 4/27/16. Subhransu Maji Slides credit: Derek Hoiem, University of Illinois
4/27/16 Kinect Device How the Kinect Works T2 Subhransu Maji Slides credit: Derek Hoiem, University of Illinois Photo frame-grabbed from: http://www.blisteredthumbs.net/2010/11/dance-central-angry-review
More informationLight Transport CS434. Daniel G. Aliaga Department of Computer Science Purdue University
Light Transport CS434 Daniel G. Aliaga Department of Computer Science Purdue University Topics Local and Global Illumination Models Helmholtz Reciprocity Dual Photography/Light Transport (in Real-World)
More information3D Shape Recovery of Smooth Surfaces: Dropping the Fixed Viewpoint Assumption
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO., 1 3D Shape Recovery of Smooth Surfaces: Dropping the Fixed Viewpoint Assumption Yael Moses Member, IEEE and Ilan Shimshoni Member,
More information3D Computer Vision. Structured Light I. Prof. Didier Stricker. Kaiserlautern University.
3D Computer Vision Structured Light I Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Introduction
More informationStructured Light II. Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov
Structured Light II Johannes Köhler Johannes.koehler@dfki.de Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov Introduction Previous lecture: Structured Light I Active Scanning Camera/emitter
More informationMulti-view reconstruction for projector camera systems based on bundle adjustment
Multi-view reconstruction for projector camera systems based on bundle adjustment Ryo Furuakwa, Faculty of Information Sciences, Hiroshima City Univ., Japan, ryo-f@hiroshima-cu.ac.jp Kenji Inose, Hiroshi
More informationAnd if that 120MP Camera was cool
Reflectance, Lights and on to photometric stereo CSE 252A Lecture 7 And if that 120MP Camera was cool Large Synoptic Survey Telescope 3.2Gigapixel camera 189 CCD s, each with 16 megapixels Pixels are 10µm
More informationOverview of Active Vision Techniques
SIGGRAPH 99 Course on 3D Photography Overview of Active Vision Techniques Brian Curless University of Washington Overview Introduction Active vision techniques Imaging radar Triangulation Moire Active
More informationIntroduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation
Introduction CMPSCI 591A/691A CMPSCI 570/670 Image Formation Lecture Outline Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic
More informationCAP 5415 Computer Vision. Fall 2011
CAP 5415 Computer Vision Fall 2011 General Instructor: Dr. Mubarak Shah Email: shah@eecs.ucf.edu Office: 247-F HEC Course Class Time Tuesdays, Thursdays 12 Noon to 1:15PM 383 ENGR Office hours Tuesdays
More information