Data Acquisition, Leica Scan Station 2, Park Avenue and 70 th Street, NY
|
|
- Lionel Stokes
- 5 years ago
- Views:
Transcription
1 Automated registration of 3D-range with 2D-color images: an overview 44 th Annual Conference on Information Sciences and Systems Invited Session: 3D Data Acquisition and Analysis March 19 th 2010 Ioannis Stamos City University it of New York Hunter College & Graduate Center Data Acquisition, Leica Scan Station 2, Park Avenue and 70 th Street, NY
2 Park Avenue: Six registered scans (top view) Brightness Images System Overview Range Images 2D feature extraction Segmentation 3D feature extraction Partial Model Range-Intensity Registration Range-Range Registration Complete Model FINAL PHOTOREALISTIC MODEL FINAL MODEL
3 Range-Intensity Registration Fixed relative positioning - limitations 2D sensor is limited by 3D scanner 3D scanning slow/laborious process 2D image capture fast/flexible 2D sensor cannot dynamically adjust parameters on site Cannot handle historical photographs
4 Automated methods One 2D image Untextured 3D model Ikeuchi 03 (edges reflectance image) Troccoli & Allen 04 (shadows) Stamos & Allen 01 (rectangles) Liu, Stamos 05 (parallelepipeds) Liu, Stamos 07(lines) One 2D image - Textured 3D model Yang, Stewart etal. 07 Schindler (regularities) Multiple 2D images (SfM/stereo) 3D model Liu, Stamos 06, Zhao etal. 05 Aerial 2D images 3D model Ding 08, Fisher 09, Neumann 09, Kaminsky 09. Input: Range Images System Outline (Liu, Stamos) Input: 2D Images 3D Line Extraction Range Registration 3D Line Clustering 3D Feature Extraction 2D Line Extraction Vanishing Point Extraction Camera Calibration and Rectification 2D Feature Extraction Automatic Features Matching and Translation Computation Final Transformation Optimization
5 3D feature extraction, Thomas Hunter Building 3D Line Extraction Clustered 3D Lines (3 major directions) 3D Face Extraction Range-Range Registration 3 Faces Extracted (Front, Left and Right) 3D Feature Extraction Line Clustering All 3D Lines Extracted Rectangular Parallelepipeds Clustering lines into vertical/horizontal parallelepipeds
6 System Outline (Liu, Stamos) Input: Range Images Input: 2D Images 3D Line Extraction Range Registration 3D Line Clustering 3D Feature Extraction 2D Line Extraction Vanishing Points Extraction Camera Calibration and Rectification 2D Feature Extraction Automatic Features Matching and Translation Computation Final Transformation Optimization 2D FEATURE EXTRACTION 2D Line Extraction 2D Feature Extraction VPs Extraction Rectified 2D Lines Camera Calibration and Rectification Matching between VPs and 3D scene major directions Extracted Rectangles Rotation between camera and 3D scene
7 Vanishing Points (2D line clustering) Computing Vanishing Points
8 Solving for the rotation Clustering 2D lines from 2D image into horizontal/vertical rectangles
9 3D Features 2D Features
10 Translation computation from a matched 3D-2D pair Matching 3D parallelepipeds with 2D rectangles 3D parallelepipeds 2D rectangles Matches shown in blue
11 The performance on Thomas Hunter Building FP CM G OP (%) RDT (%) TIME (sec) 117 x x x x x x x x x x x Recovered camera locations wrt texture-mapped point model
12 Recovered camera locations wrt texture-mapped point model Virtual (synthetic) scene views
13 Virtual (synthetic) scene views Virtual (synthetic) scene views
14 Discussion Advantages Accurate Limited user interaction 2D plane to 3D plane matching Limitations Requires major facades Higher-order features decrease generality Non-interactive speed System II Outline (lines) Input: Range Images Input: 2D Images 3D Line Extraction Range Registration 3D Line Clustering 3D Feature Extraction 2D Line Extraction Vanishing Points Extraction Camera Calibration and Rotation Computation 2D Feature Extraction Automated Feature Matching and Translation Computation Final Transformation Optimization
15 Feature Detection User Interaction Z w Y w O w X w OpenGL Frame Y i Z c P Y c f X i C X c C o
16 Matching VPs and 3D dirs Image Plane D 3D a α D 3D b V a V b Z c Y c P β f O w Y w C Z w X w X c Initial List of Candidate Matches D 3D a D 3D b V a V b D 3D c D 3D d V a D 3D a V b D 3D c D 3D b Refined List of Candidate Matches D 3D d
17 Focal length computation D 3D a α D 3D b Automatic Feature Matching and Translation Computation 3D Line Feature Get 2 pairs (l 3D a, l 2D a) and (l 3D b, l 2D b) 2D Line Feature Compute Translation C(l 3D a,l 2D a) Compute overlap O(l 3D b, l 2D b) by applying C(l 3D a, l 2D a) O(l 3D b, l 2D b) > Oth Compute Translation C(l 3D b, l 2D b) O(l 3D b, l 2D b) < Oth Compute overlap O(l 3D O(l 3D a, l 2D a) < Oth a, l 2D a) by applying C(l 3D b, l 2D b) No more pairs Grade each Ci in T Gi > Gth Optimization of Ci O(l 3D a, l 2D a) > Oth Candidate Trans C is the weighted average of C(l 3D a, l 2D a) and C(l 3D b, l 2D b). Store C in list T Final Translation
18 Translation computation: 3D-2D line match Image Plane A l 3D a S Y c l 2D a B Z c T C O w Y w X c R X w Z w Metric to be minimized for transform computation Kumar & Hanson
19 GCT 3D scene 2D image GCT User interaction 2D image
20 GCT Text. mapped 3D model (automated) 2D image GCT Text. mapped 3D model (automated) 2D image Corresponding 2D/proj. 3D lines
21 GCT 3D scene 2D image GCT Texture mapped 3D model Corresponding 2D/proj. 3D lines
22 Evaluation Selected 3D lines from 3D cloud Selected 2D lines Projection of 3D lines on 2D img Calculate error
23 3D lines 2D lines focal (init) focal (comp) Matches Error (pixels) Thomas Hunter Astor Place Grand Central Terminal
24 2D-image to 3D-range Registration Structure From Motion Range Images Many images CVPR, IJCV Unregistered Point Clouds 3D-range to 3D-SFM Registration Texture Mapping SFM Point Cloud Two Registered Point Clouds Final Output MULTIVIEW EXAMPLE
25 POSE AND STRUCTURE RECOVERY 3D Structure X j Common 2D-2D.. Frame 0 K[I 0] Frame 1 K[R 1 T 1 ] New Structure Frame 2 K[R 2 T 2 ] Frame N K[R N T N ] Initial pose and structure Compute [Ri Ti] using 6-point RANSAC Sequential updating to recover pose and structure 84 STRUCTURE AND MOTION REFINEMENT Bundle adjustment Minimize min reprojection D m error:,pˆ Mˆ m n Pˆ,Mˆ k i k 1 i 1 ki k i 2 Maximum Likelihood Estimation (assume error is zero-mean Gaussian noise) Huge problem but can be solved efficiently (use sparse bundle adjustment) 85
26 ALIGNING RANGE AND SFM POINT CLOUDS Range and SFM models Range model Range with overlaid SFM model (red)
27 3D-RANGE TO 3D-SFM REGISTRATION 3D-SFM points projected onto image 3D-Range points projected on same image Finding corresponding points between 3D-range and 3D-SFM models is possible on the 2D-image space 3D-RANGE TO 3D-SFM REGISTRATION 3D-SFM points projected onto image 3D-Range points projected on same image x :Projection of SFM point X x: Projection of SFM point X y : Projection of range point Y y: Projection of range point Y (X,Y) and (X,Y ) are candidate matches: y is the closest point to x and y the closest point to x in 2D-image space
28 3D-RANGE TO 3D-SFM REGISTRATION Candidate matches (X,Y) and (X,Y ) are compatible iff the following scale factors are equal: 1. s 0 = X-C sfm / Y-C range 2. s 1 = X -C sfm / Y -C range 3. s 2 = X -X / Y -Y where C sfm is the COP wrt the SFM model and C range is the COP wrt the range model Confidence of (X,Y) := # compatible matches (X,Y ). Match (X,Y) with largest confidence among all 2D images provides the optimal scale factor s opt List C of robust matches := {(X,Y) such that s 0 s opt }U{(C sfm, C range ) of images} Scale, rotation, and translation computed by minimizing QUANTITATIVE RESULTS 91
29 Single Image to 3D range Structure-from-motion approach GRAND CENTRAL (1)
30 GRAND CENTRAL (2) CONCLUSIONS Integration of multiview geometry with range registration 2D-to-3D registration is used for a subset of images that contain a sufficient number of linear features Brings these images into alignment with the dense 3D-range model Multiview geometry exploits 2D-point correspondences Brings all images into alignment and produces sparse SFM model 3D-range to 3D-SFM registration Aligns all images with the dense 3D-range model Accurate texture mapping onto dense 3D-range data Limitations: Need accurate vanishing points (3D-2D registration) Need accurate 3D features Need to handle symmetries
31 Credits Collaborators: Dr. Cecilia Chao Chen (Google, Inc.) Dr. Lingyun Liu (Google, Inc.) Dr. Siavash Zokai Dr. Gene Yu Prof. George Wolberg (CCNY) Funding: NSF CAREER,MRI,RI,MSC Awards PSC-CUNY Grants CUNY Institute of Software Design & Development Thank you
32 Automated registration of 3D-range with 2D-color images: an overview 44 th Annual Conference on Information Sciences and Systems Invited Session: 3D Data Acquisition and Analysis March 19 th 2010 Ioannis Stamos City University it of New York Hunter College & Graduate Center
Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes
Click here to download Manuscript: all.tex 1 1 1 1 1 0 1 0 1 Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes Ioannis Stamos, Lingyun
More informationAUTOMATED 3D MODELING OF URBAN ENVIRONMENTS
AUTOMATED 3D MODELING OF URBAN ENVIRONMENTS Ioannis Stamos Department of Computer Science Hunter College, City University of New York 695 Park Avenue, New York NY 10065 istamos@hunter.cuny.edu http://www.cs.hunter.cuny.edu/
More informationEstimation of Camera Pose with Respect to Terrestrial LiDAR Data
Estimation of Camera Pose with Respect to Terrestrial LiDAR Data Wei Guan Suya You Guan Pang Computer Science Department University of Southern California, Los Angeles, USA Abstract In this paper, we present
More informationSemi-automatic range to range registration: a feature-based method
Semi-automatic range to range registration: a feature-based method Chen Chao Graduate Center of CUNY, New York, NY, CChen@gc.cuny.edu Ioannis Stamos Hunter College and Graduate Center of CUNY, New York,
More informationCamera Drones Lecture 3 3D data generation
Camera Drones Lecture 3 3D data generation Ass.Prof. Friedrich Fraundorfer WS 2017 Outline SfM introduction SfM concept Feature matching Camera pose estimation Bundle adjustment Dense matching Data products
More informationCSc Topics in Computer Graphics 3D Photography
CSc 83010 Topics in Computer Graphics 3D Photography Tuesdays 11:45-1:45 1:45 Room 3305 Ioannis Stamos istamos@hunter.cuny.edu Office: 1090F, Hunter North (Entrance at 69 th bw/ / Park and Lexington Avenues)
More informationIntegrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes
Int J Comput Vis (2008) 78: 237 260 DOI 10.1007/s11263-007-0089-1 Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes Ioannis Stamos Lingyun
More informationAutomatic Registration of LiDAR and Optical Imagery using Depth Map Stereo
Automatic Registration of LiDAR and Optical Imagery using Depth Map Stereo Hyojin Kim Lawrence Livermore National Laboratory kim63@llnl.gov Carlos D. Correa Google Inc. cdcorrea@google.com Nelson Max University
More informationFAST REGISTRATION OF TERRESTRIAL LIDAR POINT CLOUD AND SEQUENCE IMAGES
FAST REGISTRATION OF TERRESTRIAL LIDAR POINT CLOUD AND SEQUENCE IMAGES Jie Shao a, Wuming Zhang a, Yaqiao Zhu b, Aojie Shen a a State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing
More information3D Computer Vision. Structured Light II. Prof. Didier Stricker. Kaiserlautern University.
3D Computer Vision Structured Light II Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Introduction
More informationGeometry and Texture Recovery of Scenes of Large Scale
Computer Vision and Image Understanding 88, 94 118 (2002) doi:10.1006/cviu.2002.0963 Geometry and Texture Recovery of Scenes of Large Scale Ioannis Stamos Computer Science Department, Hunter College and
More informationAutomatic registration of 2 D with 3 D imagery in urban environments. 3 D rectangular structures from the range data and 2 D
Automatic registration of 2 D with 3 D imagery in urban environments Ioannis Stamos and Peter K. Allen [Submitted for publication to ICCV 20] Abstract We are building a system that can automatically acquire
More information1 Projective Geometry
CIS8, Machine Perception Review Problem - SPRING 26 Instructions. All coordinate systems are right handed. Projective Geometry Figure : Facade rectification. I took an image of a rectangular object, and
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 informationCamera Registration in a 3D City Model. Min Ding CS294-6 Final Presentation Dec 13, 2006
Camera Registration in a 3D City Model Min Ding CS294-6 Final Presentation Dec 13, 2006 Goal: Reconstruct 3D city model usable for virtual walk- and fly-throughs Virtual reality Urban planning Simulation
More informationStep-by-Step Model Buidling
Step-by-Step Model Buidling Review Feature selection Feature selection Feature correspondence Camera Calibration Euclidean Reconstruction Landing Augmented Reality Vision Based Control Sparse Structure
More informationarxiv: v1 [cs.cv] 28 Sep 2018
Camera Pose Estimation from Sequence of Calibrated Images arxiv:1809.11066v1 [cs.cv] 28 Sep 2018 Jacek Komorowski 1 and Przemyslaw Rokita 2 1 Maria Curie-Sklodowska University, Institute of Computer Science,
More information3D Modeling Using Planar Segments And Mesh Elements. Gene Yu and George Wolberg. City College / CUNY New York, NY 10031
3D Modeling Using Planar Segments And Mesh Elements Ioannis Stamos Dept. of Computer Science Hunter College / CUNY New York, NY 10021 istamos@hunter.cuny.edu Gene Yu and George Wolberg Dept. of Computer
More information3D Reconstruction from Scene Knowledge
Multiple-View Reconstruction from Scene Knowledge 3D Reconstruction from Scene Knowledge SYMMETRY & MULTIPLE-VIEW GEOMETRY Fundamental types of symmetry Equivalent views Symmetry based reconstruction MUTIPLE-VIEW
More informationLecture 8.2 Structure from Motion. Thomas Opsahl
Lecture 8.2 Structure from Motion Thomas Opsahl More-than-two-view geometry Correspondences (matching) More views enables us to reveal and remove more mismatches than we can do in the two-view case More
More informationLow-Rank Based Algorithms for Rectification, Repetition Detection and De-Noising in Urban Images. A dissertation proposal by Juan Liu
Low-Rank Based Algorithms for Rectification, Repetition Detection and De-Noising in Urban Images A dissertation proposal by Juan Liu Committee Committee: Professor Ioannis Stamos, Mentor, Hunter College
More informationREFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES
REFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES T. Yamakawa a, K. Fukano a,r. Onodera a, H. Masuda a, * a Dept. of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications,
More informationRange Image Segmentation for Modeling and Object Detection in Urban Scenes
Range Image Segmentation for Modeling and Object Detection in Urban Scenes Cecilia Chao Chen Ioannis Stamos Graduate Center / CUNY Hunter College / CUNY New York, NY 10016 New York, NY 10021 cchen@gc.cuny.edu
More informationCOMPARISON OF LASER SCANNING, PHOTOGRAMMETRY AND SfM-MVS PIPELINE APPLIED IN STRUCTURES AND ARTIFICIAL SURFACES
COMPARISON OF LASER SCANNING, PHOTOGRAMMETRY AND SfM-MVS PIPELINE APPLIED IN STRUCTURES AND ARTIFICIAL SURFACES 2012 ISPRS Melbourne, Com III/4, S.Kiparissi Cyprus University of Technology 1 / 28 Structure
More informationImage Rectification (Stereo) (New book: 7.2.1, old book: 11.1)
Image Rectification (Stereo) (New book: 7.2.1, old book: 11.1) Guido Gerig CS 6320 Spring 2013 Credits: Prof. Mubarak Shah, Course notes modified from: http://www.cs.ucf.edu/courses/cap6411/cap5415/, Lecture
More informationAutomated Feature-Based Range Registration of Urban Scenes of Large Scale
Automated Feature-Based Range Registration of Urban Scenes of Large Scale Ioannis Stamos and Marius Leordeanu Department of Computer Science, Hunter College, City University of New York, NY, NY 1001 {istamos,mleordeanu}@hunter.cuny.edu
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 informationGeometry based Repetition Detection for Urban Scene
Geometry based Repetition Detection for Urban Scene Changchang Wu University of Washington Jan Michael Frahm UNC Chapel Hill Marc Pollefeys ETH Zürich Related Work Sparse Feature Matching [Loy et al. 06,
More informationMultiview Stereo COSC450. Lecture 8
Multiview Stereo COSC450 Lecture 8 Stereo Vision So Far Stereo and epipolar geometry Fundamental matrix captures geometry 8-point algorithm Essential matrix with calibrated cameras 5-point algorithm Intersect
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 informationMultiview Photogrammetry 3D Virtual Geology for everyone
Multiview Photogrammetry 3D Virtual Geology for everyone A short course Marko Vrabec University of Ljubljana, Department of Geology FIRST: some background info Precarious structural measurements of fractures
More informationReal-time Image-based Reconstruction of Pipes Using Omnidirectional Cameras
Real-time Image-based Reconstruction of Pipes Using Omnidirectional Cameras Dipl. Inf. Sandro Esquivel Prof. Dr.-Ing. Reinhard Koch Multimedia Information Processing Christian-Albrechts-University of Kiel
More informationOmni Stereo Vision of Cooperative Mobile Robots
Omni Stereo Vision of Cooperative Mobile Robots Zhigang Zhu*, Jizhong Xiao** *Department of Computer Science **Department of Electrical Engineering The City College of the City University of New York (CUNY)
More informationPhoto Tourism: Exploring Photo Collections in 3D
Photo Tourism: Exploring Photo Collections in 3D SIGGRAPH 2006 Noah Snavely Steven M. Seitz University of Washington Richard Szeliski Microsoft Research 2006 2006 Noah Snavely Noah Snavely Reproduced with
More informationCapture and Dewarping of Page Spreads with a Handheld Compact 3D Camera
Capture and Dewarping of Page Spreads with a Handheld Compact 3D Camera Michael P. Cutter University of California at Santa Cruz Baskin School of Engineering (Computer Engineering department) Santa Cruz,
More informationEstimating the Location of a Camera with Respect to a 3D Model
Estimating the Location of a Camera with Respect to a 3D Model Gehua Yang Jacob Becker Charles V. Stewart Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180, U.S.A. {yangg2,beckej,stewart}@cs.rpi.edu
More informationA New Representation for Video Inspection. Fabio Viola
A New Representation for Video Inspection Fabio Viola Outline Brief introduction to the topic and definition of long term goal. Description of the proposed research project. Identification of a short term
More informationPerspective projection
Sensors Ioannis Stamos Perspective projection Pinhole & the Perspective Projection (x,y) SCENE SCREEN Is there an image being formed on the screen?
More informationStructure from Motion. Introduction to Computer Vision CSE 152 Lecture 10
Structure from Motion CSE 152 Lecture 10 Announcements Homework 3 is due May 9, 11:59 PM Reading: Chapter 8: Structure from Motion Optional: Multiple View Geometry in Computer Vision, 2nd edition, Hartley
More informationHomographies and RANSAC
Homographies and RANSAC Computer vision 6.869 Bill Freeman and Antonio Torralba March 30, 2011 Homographies and RANSAC Homographies RANSAC Building panoramas Phototourism 2 Depth-based ambiguity of position
More informationAIT Inline Computational Imaging: Geometric calibration and image rectification
AIT Inline Computational Imaging: Geometric calibration and image rectification B. Blaschitz, S. Štolc and S. Breuss AIT Austrian Institute of Technology GmbH Center for Vision, Automation & Control Vienna,
More informationHigh Definition Modeling of Calw, Badstrasse and its Google Earth Integration
Master Thesis Yuanting LI High Definition Modeling of Calw, Badstrasse and its Google Earth Integration Duration of the Thesis: 6 months Completion: July, 2014 Supervisors: Prof.Dr.-Ing.Dieter Fritsch
More informationModel Selection for Automated Architectural Reconstruction from Multiple Views
Model Selection for Automated Architectural Reconstruction from Multiple Views Tomáš Werner, Andrew Zisserman Visual Geometry Group, University of Oxford Abstract We describe progress in automatically
More informationTextured Mesh Surface Reconstruction of Large Buildings with Multi-View Stereo
The Visual Computer manuscript No. (will be inserted by the editor) Textured Mesh Surface Reconstruction of Large Buildings with Multi-View Stereo Chen Zhu Wee Kheng Leow Received: date / Accepted: date
More informationA Vision-based 2D-3D Registration System
A Vision-based 2D-3D Registration System Quan Wang, Suya You CGIT/IMSC USC Los Angeles, CA 90089 quanwang@usc.edu, suyay@graphics.usc.edu Abstract In this paper, we propose an automatic system for robust
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 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 informationReal Time Detection of Repeated Structures in Point Clouds of Urban Scenes
Real Time Detection of Repeated Structures in Point Clouds of Urban Scenes Sam Friedman Hunter College & Graduate Center 695 Park Ave, New York NY, 10021 http:// www.welike2draw.com/ vision/ Ioannis Stamos
More informationAutomatic registration of aerial imagery with untextured 3D LiDAR models
Automatic registration of aerial imagery with untextured 3D LiDAR models Min Ding, Kristian Lyngbaek and Avideh Zakhor University of California, Berkeley Electrical Engineering and Computer Science Department
More informationA Systems View of Large- Scale 3D Reconstruction
Lecture 23: A Systems View of Large- Scale 3D Reconstruction Visual Computing Systems Goals and motivation Construct a detailed 3D model of the world from unstructured photographs (e.g., Flickr, Facebook)
More informationImage correspondences and structure from motion
Image correspondences and structure from motion http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 20 Course announcements Homework 5 posted.
More informationQuality Report Generated with version
Quality Report Generated with version 3.3.67 Important: Click on the different icons for: Help to analyze the results in the Quality Report Additional information about the feature Click here for additional
More informationQuality Report Generated with version
Quality Report Generated with version 1.3.54 Important: Click on the different icons for: Help to analyze the results in the Quality Report Additional information about the feature Click here for additional
More informationSimultaneous Localization and Mapping (SLAM)
Simultaneous Localization and Mapping (SLAM) RSS Lecture 16 April 8, 2013 Prof. Teller Text: Siegwart and Nourbakhsh S. 5.8 SLAM Problem Statement Inputs: No external coordinate reference Time series of
More informationcalibrated coordinates Linear transformation pixel coordinates
1 calibrated coordinates Linear transformation pixel coordinates 2 Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration with partial
More informationA Desktop 3D Scanner Exploiting Rotation and Visual Rectification of Laser Profiles
A Desktop 3D Scanner Exploiting Rotation and Visual Rectification of Laser Profiles Carlo Colombo, Dario Comanducci, and Alberto Del Bimbo Dipartimento di Sistemi ed Informatica Via S. Marta 3, I-5139
More informationBinocular Stereo Vision. System 6 Introduction Is there a Wedge in this 3D scene?
System 6 Introduction Is there a Wedge in this 3D scene? Binocular Stereo Vision Data a stereo pair of images! Given two 2D images of an object, how can we reconstruct 3D awareness of it? AV: 3D recognition
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 informationMiniature faking. In close-up photo, the depth of field is limited.
Miniature faking In close-up photo, the depth of field is limited. http://en.wikipedia.org/wiki/file:jodhpur_tilt_shift.jpg Miniature faking Miniature faking http://en.wikipedia.org/wiki/file:oregon_state_beavers_tilt-shift_miniature_greg_keene.jpg
More informationImproving Initial Estimations for Structure from Motion Methods
Improving Initial Estimations for Structure from Motion Methods University of Bonn Outline Motivation Computer-Vision Basics Stereo Vision Bundle Adjustment Feature Matching Global Initial Estimation Component
More informationRecap: Features and filters. Recap: Grouping & fitting. Now: Multiple views 10/29/2008. Epipolar geometry & stereo vision. Why multiple views?
Recap: Features and filters Epipolar geometry & stereo vision Tuesday, Oct 21 Kristen Grauman UT-Austin Transforming and describing images; textures, colors, edges Recap: Grouping & fitting Now: Multiple
More informationImage warping and stitching
Image warping and stitching Thurs Oct 15 Last time Feature-based alignment 2D transformations Affine fit RANSAC 1 Robust feature-based alignment Extract features Compute putative matches Loop: Hypothesize
More informationLarge Scale 3D Reconstruction by Structure from Motion
Large Scale 3D Reconstruction by Structure from Motion Devin Guillory Ziang Xie CS 331B 7 October 2013 Overview Rome wasn t built in a day Overview of SfM Building Rome in a Day Building Rome on a Cloudless
More informationMidterm Examination CS 534: Computational Photography
Midterm Examination CS 534: Computational Photography November 3, 2016 NAME: Problem Score Max Score 1 6 2 8 3 9 4 12 5 4 6 13 7 7 8 6 9 9 10 6 11 14 12 6 Total 100 1 of 8 1. [6] (a) [3] What camera setting(s)
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 informationSimultaneous Localization
Simultaneous Localization and Mapping (SLAM) RSS Technical Lecture 16 April 9, 2012 Prof. Teller Text: Siegwart and Nourbakhsh S. 5.8 Navigation Overview Where am I? Where am I going? Localization Assumed
More informationCIS 580, Machine Perception, Spring 2015 Homework 1 Due: :59AM
CIS 580, Machine Perception, Spring 2015 Homework 1 Due: 2015.02.09. 11:59AM Instructions. Submit your answers in PDF form to Canvas. This is an individual assignment. 1 Camera Model, Focal Length and
More informationPERFORMANCE CAPTURE FROM SPARSE MULTI-VIEW VIDEO
Stefan Krauß, Juliane Hüttl SE, SoSe 2011, HU-Berlin PERFORMANCE CAPTURE FROM SPARSE MULTI-VIEW VIDEO 1 Uses of Motion/Performance Capture movies games, virtual environments biomechanics, sports science,
More informationCamera Geometry II. COS 429 Princeton University
Camera Geometry II COS 429 Princeton University Outline Projective geometry Vanishing points Application: camera calibration Application: single-view metrology Epipolar geometry Application: stereo correspondence
More informationEVOLUTION OF POINT CLOUD
Figure 1: Left and right images of a stereo pair and the disparity map (right) showing the differences of each pixel in the right and left image. (source: https://stackoverflow.com/questions/17607312/difference-between-disparity-map-and-disparity-image-in-stereo-matching)
More informationImage warping and stitching
Image warping and stitching May 4 th, 2017 Yong Jae Lee UC Davis Last time Interactive segmentation Feature-based alignment 2D transformations Affine fit RANSAC 2 Alignment problem In alignment, we will
More informationFace Recognition At-a-Distance Based on Sparse-Stereo Reconstruction
Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction Ham Rara, Shireen Elhabian, Asem Ali University of Louisville Louisville, KY {hmrara01,syelha01,amali003}@louisville.edu Mike Miller,
More informationMETRIC PLANE RECTIFICATION USING SYMMETRIC VANISHING POINTS
METRIC PLANE RECTIFICATION USING SYMMETRIC VANISHING POINTS M. Lefler, H. Hel-Or Dept. of CS, University of Haifa, Israel Y. Hel-Or School of CS, IDC, Herzliya, Israel ABSTRACT Video analysis often requires
More informationStereo and Epipolar geometry
Previously Image Primitives (feature points, lines, contours) Today: Stereo and Epipolar geometry How to match primitives between two (multiple) views) Goals: 3D reconstruction, recognition Jana Kosecka
More informationDense 3D Modelling and Monocular Reconstruction of Deformable Objects
Dense 3D Modelling and Monocular Reconstruction of Deformable Objects Anastasios (Tassos) Roussos Lecturer in Computer Science, University of Exeter Research Associate, Imperial College London Overview
More informationImage stitching. Announcements. Outline. Image stitching
Announcements Image stitching Project #1 was due yesterday. Project #2 handout will be available on the web later tomorrow. I will set up a webpage for artifact voting soon. Digital Visual Effects, Spring
More informationFast and robust techniques for 3D/2D registration and photo blending on massive point clouds
www.crs4.it/vic/ vcg.isti.cnr.it/ Fast and robust techniques for 3D/2D registration and photo blending on massive point clouds R. Pintus, E. Gobbetti, M.Agus, R. Combet CRS4 Visual Computing M. Callieri
More informationQuality assessment and comparison of smartphone, airborne and leica c10 laser scanner based point clouds
Delft University of Technology Quality assessment and comparison of smartphone, airborne and leica c10 laser scanner based point clouds Sirmacek, Beril; Lindenbergh, Roderik; Wang, Jinhu DOI 10.5194/isprs-archives-XLI-B5-581-2016
More informationImage warping and stitching
Image warping and stitching May 5 th, 2015 Yong Jae Lee UC Davis PS2 due next Friday Announcements 2 Last time Interactive segmentation Feature-based alignment 2D transformations Affine fit RANSAC 3 Alignment
More informationCSE 252B: Computer Vision II
CSE 252B: Computer Vision II Lecturer: Serge Belongie Scribes: Jeremy Pollock and Neil Alldrin LECTURE 14 Robust Feature Matching 14.1. Introduction Last lecture we learned how to find interest points
More informationProject 4 Results. Representation. Data. Learning. Zachary, Hung-I, Paul, Emanuel. SIFT and HoG are popular and successful.
Project 4 Results Representation SIFT and HoG are popular and successful. Data Hugely varying results from hard mining. Learning Non-linear classifier usually better. Zachary, Hung-I, Paul, Emanuel Project
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 informationCS 4495 Computer Vision A. Bobick. Motion and Optic Flow. Stereo Matching
Stereo Matching Fundamental matrix Let p be a point in left image, p in right image l l Epipolar relation p maps to epipolar line l p maps to epipolar line l p p Epipolar mapping described by a 3x3 matrix
More informationarxiv: v1 [cs.cv] 28 Sep 2018
Extrinsic camera calibration method and its performance evaluation Jacek Komorowski 1 and Przemyslaw Rokita 2 arxiv:1809.11073v1 [cs.cv] 28 Sep 2018 1 Maria Curie Sklodowska University Lublin, Poland jacek.komorowski@gmail.com
More informationA Low Power, High Throughput, Fully Event-Based Stereo System: Supplementary Documentation
A Low Power, High Throughput, Fully Event-Based Stereo System: Supplementary Documentation Alexander Andreopoulos, Hirak J. Kashyap, Tapan K. Nayak, Arnon Amir, Myron D. Flickner IBM Research March 25,
More informationChapter 3 Image Registration. Chapter 3 Image Registration
Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation
More informationPlanetary Rover Absolute Localization by Combining Visual Odometry with Orbital Image Measurements
Planetary Rover Absolute Localization by Combining Visual Odometry with Orbital Image Measurements M. Lourakis and E. Hourdakis Institute of Computer Science Foundation for Research and Technology Hellas
More informationFeature Transfer and Matching in Disparate Stereo Views through the use of Plane Homographies
Feature Transfer and Matching in Disparate Stereo Views through the use of Plane Homographies M. Lourakis, S. Tzurbakis, A. Argyros, S. Orphanoudakis Computer Vision and Robotics Lab (CVRL) Institute of
More information28 out of 28 images calibrated (100%), all images enabled. 0.02% relative difference between initial and optimized internal camera parameters
Dronedata Render Server Generated Quality Report Phase 1 Time 00h:01m:56s Phase 2 Time 00h:04m:35s Phase 3 Time 00h:13m:45s Total Time All Phases 00h:20m:16s Generated with Pix4Dmapper Pro - TRIAL version
More informationObject Recognition with Invariant Features
Object Recognition with Invariant Features Definition: Identify objects or scenes and determine their pose and model parameters Applications Industrial automation and inspection Mobile robots, toys, user
More informationSupplementary Note to Detecting Building-level Changes of a City Using Street Images and a 2D City Map
Supplementary Note to Detecting Building-level Changes of a City Using Street Images and a 2D City Map Daiki Tetsuka Takayuki Okatani Graduate School of Information Sciences, Tohoku University okatani@vision.is.tohoku.ac.jp
More informationRecognizing Buildings in Urban Scene of Distant View ABSTRACT
Recognizing Buildings in Urban Scene of Distant View Peilin Liu, Katsushi Ikeuchi and Masao Sakauchi Institute of Industrial Science, University of Tokyo, Japan 7-22-1 Roppongi, Minato-ku, Tokyo 106, Japan
More informationCamera Calibration. COS 429 Princeton University
Camera Calibration COS 429 Princeton University Point Correspondences What can you figure out from point correspondences? Noah Snavely Point Correspondences X 1 X 4 X 3 X 2 X 5 X 6 X 7 p 1,1 p 1,2 p 1,3
More informationOutline of Presentation. Introduction to Overwatch Geospatial Software Feature Analyst and LIDAR Analyst Software
Outline of Presentation Automated Feature Extraction from Terrestrial and Airborne LIDAR Presented By: Stuart Blundell Overwatch Geospatial - VLS Ops Co-Author: David W. Opitz Overwatch Geospatial - VLS
More informationQuality Report Generated with Pix4Dmapper Pro version
Quality Report Generated with Pix4Dmapper Pro version 3.1.23 Important: Click on the different icons for: Help to analyze the results in the Quality Report Additional information about the sections Click
More informationLecture 10: Multi-view geometry
Lecture 10: Multi-view geometry Professor Stanford Vision Lab 1 What we will learn today? Review for stereo vision Correspondence problem (Problem Set 2 (Q3)) Active stereo vision systems Structure from
More informationA Factorization Method for Structure from Planar Motion
A Factorization Method for Structure from Planar Motion Jian Li and Rama Chellappa Center for Automation Research (CfAR) and Department of Electrical and Computer Engineering University of Maryland, College
More informationBIL Computer Vision Apr 16, 2014
BIL 719 - Computer Vision Apr 16, 2014 Binocular Stereo (cont d.), Structure from Motion Aykut Erdem Dept. of Computer Engineering Hacettepe University Slide credit: S. Lazebnik Basic stereo matching algorithm
More informationSampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images
Sampling based Bundle Adjustment using Feature Matches between Ground-view and Aerial Images Hideyuki Kume, Tomokazu Sato and Naokazu Yokoya Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma,
More informationClustering. Robert M. Haralick. Computer Science, Graduate Center City University of New York
Clustering Robert M. Haralick Computer Science, Graduate Center City University of New York Outline K-means 1 K-means 2 3 4 5 Clustering K-means The purpose of clustering is to determine the similarity
More information