A Laser Range Scanner Designed for Minimum Calibration Complexity James Davis, Xing Chen Stanford Computer Graphics Laboratory 3D Digital Imaging and Modeling.

Slides:



Advertisements
Similar presentations
Structured Light principles Figure from M. Levoy, Stanford Computer Graphics Lab.
Advertisements

Structured light and active ranging techniques Class 11.
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #17.
Stereo Many slides adapted from Steve Seitz. Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image Where does the.
Robust Global Registration Natasha Gelfand Niloy Mitra Leonidas Guibas Helmut Pottmann.
Amir Hosein Omidvarnia Spring 2007 Principles of 3D Face Recognition.
Structured Lighting Guido Gerig CS 6320, 3D Computer Vision Spring 2012 (thanks: some slides S. Narasimhan CMU, Marc Pollefeys UNC)
Computer Graphics Group Alexander Hornung Alexander Hornung and Leif Kobbelt RWTH Aachen Robust Reconstruction of Watertight 3D Models from Non-uniformly.
Laser Webcam Turntable Low cost 3D scanner Costs: Laser………….100euro Webcam………100euro Turntable……...150euro Side view Top view Object In order to perform.
Stereo.
Reverse Engineering Niloy J. Mitra.
Registration of two scanned range images using k-d tree accelerated ICP algorithm By Xiaodong Yan Dec
Gaussian KD-Tree for Fast High-Dimensional Filtering A. Adams, N. Gelfand, J. Dolson, and M. Levoy, Stanford University, SIGGRAPH 2009.
Boundary matting for view synthesis Samuel W. Hasinoff Sing Bing Kang Richard Szeliski Computer Vision and Image Understanding 103 (2006) 22–32.
1 Lecture 2 Main components of graphical systems Graphical devices.
Speed and Robustness in 3D Model Registration Szymon Rusinkiewicz Princeton University.
SIGGRAPH Course 30: Performance-Driven Facial Animation Section: Markerless Face Capture and Automatic Model Construction Part 2: Li Zhang, Columbia University.
1 MURI review meeting 09/21/2004 Dynamic Scene Modeling Video and Image Processing Lab University of California, Berkeley Christian Frueh Avideh Zakhor.
Marc Levoy 1 Szymon Rusinkiewicz 1 Matt Ginzton 1 Jeremy Ginsberg 1 Kari Pulli 1 David Koller 1 Sean Anderson 1 Jonathan Shade 2 Brian Curless 2 Lucas.
Filling Holes in Complex Surfaces using Volumetric Diffusion James Davis, Stephen Marschner, Matt Garr, Marc Levoy Stanford University First International.
Efficient Variants of the ICP Algorithm
Structured light and active ranging techniques Class 8.
Introduction to Computer Vision 3D Vision Topic 9 Stereo Vision (I) CMPSCI 591A/691A CMPSCI 570/670.
Edge Detection Today’s reading Forsyth, chapters 8, 15.1
Real-Time 3D Model Acquisition
Combining Laser Scans Yong Joo Kil 1, Boris Mederos 2, and Nina Amenta 1 1 Department of Computer Science, University of California at Davis 2 Instituto.
Computer Science Department
3D reconstruction Class 16. 3D photography course schedule Introduction Aug 24, 26(no course) Aug.31,Sep.2(no course) Sep. 7, 9(no course) Sep. 14, 16Projective.
Spectral Processing of Point-sampled Geometry
3D full object reconstruction from kinect Yoni Choukroun Elie Semmel Advisor: Yonathan Afflalo.
Object Detection Procedure CAMERA SOFTWARE LABVIEW IMAGE PROCESSING ALGORITHMS MOTOR CONTROLLERS TCP/IP
Project 1 artifact winners Project 2 questions Project 2 extra signup slots –Can take a second slot if you’d like Announcements.
 Marc Levoy IBM / IBR “The study of image-based modeling and rendering is the study of sampled representations of geometry.”
A Hierarchical Method for Aligning Warped Meshes Leslie Ikemoto 1, Natasha Gelfand 2, Marc Levoy 2 1 UC Berkeley, formerly Stanford 2 Stanford University.
 Marc Levoy IBM / IBR “The study of image-based modeling and rendering is the study of sampled representations of geometry.”
3D object capture Capture N “views” (parts of the object) –get points on surface of object –create mesh (infer connectivity) Hugues Hoppe –filter data.
Acquiring graphical models of shape and motion James Davis ISM101 May 2005.
My Research Experience Cheng Qian. Outline 3D Reconstruction Based on Range Images Color Engineering Thermal Image Restoration.
Structured light and active ranging techniques Class 8
Camera Calibration & Stereo Reconstruction Jinxiang Chai.
Context-based Surface Completion Andrei Sharf, Marc Alexa, Daniel Cohen-Or.
Automatic Registration of Color Images to 3D Geometry Computer Graphics International 2009 Yunzhen Li and Kok-Lim Low School of Computing National University.
KinectFusion : Real-Time Dense Surface Mapping and Tracking IEEE International Symposium on Mixed and Augmented Reality 2011 Science and Technology Proceedings.
MESA LAB Multi-view image stitching Guimei Zhang MESA LAB MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering, University of.
Recap from Monday Image Warping – Coordinate transforms – Linear transforms expressed in matrix form – Inverse transforms useful when synthesizing images.
Realtime 3D model construction with Microsoft Kinect and an NVIDIA Kepler laptop GPU Paul Caheny MSc in HPC 2011/2012 Project Preparation Presentation.
Stereo Many slides adapted from Steve Seitz.
Project 2 code & artifact due Friday Midterm out tomorrow (check your ), due next Fri Announcements TexPoint fonts used in EMF. Read the TexPoint.
3D Photorealistic Modeling Process. Different Sensors Scanners Local coordinate system Cameras Local camera coordinate system GPS Global coordinate system.
Stereo Many slides adapted from Steve Seitz. Binocular stereo Given a calibrated binocular stereo pair, fuse it to produce a depth image image 1image.
December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.
Lec 22: Stereo CS4670 / 5670: Computer Vision Kavita Bala.
Development of a laser slit system in LabView
Lecture 16: Stereo CS4670 / 5670: Computer Vision Noah Snavely Single image stereogram, by Niklas EenNiklas Een.
Using simplified meshes for crude registration of two partially overlapping range images Mercedes R.G.Márquez Wu Shin-Ting State University of Matogrosso.
Project 2 due today Project 3 out today Announcements TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA.
Image-Based Rendering CS 446: Real-Time Rendering & Game Technology David Luebke University of Virginia.
Registration and Alignment Speaker: Liuyu
Mobile Robot Localization and Mapping Using Range Sensor Data Dr. Joel Burdick, Dr. Stergios Roumeliotis, Samuel Pfister, Kristo Kriechbaum.
Faculty of Sciences and Technology from University of Coimbra Generating 3D Meshes from Range Data [1] Graphic Computation and Three-dimensional Modeling.
Semi-Global Matching with self-adjusting penalties
CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.
--- Stereoscopic Vision and Range Finders
--- Stereoscopic Vision and Range Finders
3D Scan Alignment Using ICP
A Volumetric Method for Building Complex Models from Range Images
Filtering Things to take away from this lecture An image as a function
How to Digitize the Natural Color
Real-time Acquisition and Rendering of Large 3D Models
--- Range Image Registration
Presentation transcript:

A Laser Range Scanner Designed for Minimum Calibration Complexity James Davis, Xing Chen Stanford Computer Graphics Laboratory 3D Digital Imaging and Modeling 3DIM 2001

2 Scanner Designs

3 Scanner Design A Quality Tradeoff Expense vs. accuracy Expense vs. accuracy Different curves possible Different curves possible Complex calibration is expensive Complex calibration is expensive Expense Accuracy Scanner Design B

4 Conventional stripe scanner Triangulation between camera and laser Triangulation between camera and laser

5 Complexities of traditional design Actuated components Actuated components Cylindrical lens precision Cylindrical lens precision Custom calibration procedure Custom calibration procedure

6 Our design Triangulation between two cameras Triangulation between two cameras No actuated components No actuated components

7 Catadioptric layout

8 Our scanner Simple components Simple components Camcorder, four mirrors, rigid mounting

9 Stripe processing Locate corresponding points Locate corresponding points Use epipolar constraint Discard ambiguous data

10 Cylindrical lens precision No precision mounting No precision mounting

11 Scanner calibration Camera model and pose [Heikkila, Silven 97] Camera model and pose [Heikkila, Silven 97] Well-studied easy calibration Well-studied easy calibration

12 Filter image Filter image Video signal noise Sub-pixel detection Sub-pixel detection Local Gaussian approximation Peak detection Maximum intensityLocal GaussianGaussian with filtering

13 Mesh coverage Video sequence defines mesh Video sequence defines mesh Stripe spacing related to laser velocity Stripe spacing related to laser velocity

14 Constructing a mesh Fill stripe sampling gaps Fill stripe sampling gaps Detect depth discontinuities Detect depth discontinuities

15 Aligning scans Iterative closest point (ICP) [Besl, McKay - Chen, Medioni 92] Iterative closest point (ICP) [Besl, McKay - Chen, Medioni 92] Global alignment [Pulli 99] Global alignment [Pulli 99]

16 Merging scans Volumetric merging (VRIP) [Curless, Levoy 96] Volumetric merging (VRIP) [Curless, Levoy 96] Hole filling Hole filling

17 Depth resolution 440 mm x 550 mm 240x240 pixels Expected depth resolution: 1.8 mm

18 Conclusion Minimal calibration complexity Minimal calibration complexity No actuated components No precision lens placement Well-studied easy calibration model