Introduction to 3D Machine Vision

Similar documents
Agenda. DLP 3D scanning Introduction DLP 3D scanning SDK Introduction Advance features for existing SDK

3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: ,

LUMS Mine Detector Project

3D Sensing. 3D Shape from X. Perspective Geometry. Camera Model. Camera Calibration. General Stereo Triangulation.

CS201 Computer Vision Camera Geometry

Structured Light. Tobias Nöll Thanks to Marc Pollefeys, David Nister and David Lowe

Stereo 3D camera supports automated storage of packaging Out of the box

The main problem of photogrammetry

: Easy 3D Calibration of laser triangulation systems. Fredrik Nilsson Product Manager, SICK, BU Vision

Multiple View Geometry

Project Title: Welding Machine Monitoring System Phase II. Name of PI: Prof. Kenneth K.M. LAM (EIE) Progress / Achievement: (with photos, if any)

3D Computer Vision. Structured Light I. Prof. Didier Stricker. Kaiserlautern University.

Computer and Machine Vision

For 3CCD/3CMOS/4CCD Line Scan Cameras. Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras

CS5670: Computer Vision

CV: 3D to 2D mathematics. Perspective transformation; camera calibration; stereo computation; and more

Stereo Vision A simple system. Dr. Gerhard Roth Winter 2012

Image Transformations & Camera Calibration. Mašinska vizija, 2018.

Surround Structured Lighting for Full Object Scanning

Advanced Digital Photography and Geometry Capture. Visual Imaging in the Electronic Age Lecture #10 Donald P. Greenberg September 24, 2015

Outline. ETN-FPI Training School on Plenoptic Sensing

Image Based Reconstruction II

Minimizing Noise and Bias in 3D DIC. Correlated Solutions, Inc.

Image-Based Rendering

Assignment 2: Stereo and 3D Reconstruction from Disparity

Stereo vision. Many slides adapted from Steve Seitz

Advanced Vision Guided Robotics. David Bruce Engineering Manager FANUC America Corporation

Robot Vision: Camera calibration

Laser sensors. Transmitter. Receiver. Basilio Bona ROBOTICA 03CFIOR

Range Sensors (time of flight) (1)

Stereo Vision Inside Tire

Stereo. 11/02/2012 CS129, Brown James Hays. Slides by Kristen Grauman

Index. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 263

Advanced Digital Photography and Geometry Capture. Visual Imaging in the Electronic Age Lecture #10 Donald P. Greenberg September 24, 2015

Today. Stereo (two view) reconstruction. Multiview geometry. Today. Multiview geometry. Computational Photography

MERGING POINT CLOUDS FROM MULTIPLE KINECTS. Nishant Rai 13th July, 2016 CARIS Lab University of British Columbia

Photogrammetry: DTM Extraction & Editing

3D Computer Vision. Depth Cameras. Prof. Didier Stricker. Oliver Wasenmüller

APPROACH TO ACCURATE PHOTOREALISTIC MODEL GENERATION FOR COMPLEX 3D OBJECTS

10/5/09 1. d = 2. Range Sensors (time of flight) (2) Ultrasonic Sensor (time of flight, sound) (1) Ultrasonic Sensor (time of flight, sound) (2) 4.1.

Precise laser-based optical 3D measurement of welding seams under water

Omni Stereo Vision of Cooperative Mobile Robots

Project 2 due today Project 3 out today. Readings Szeliski, Chapter 10 (through 10.5)

Camera Calibration. COS 429 Princeton University

Camera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration

Lecture'9'&'10:'' Stereo'Vision'

Sensing Deforming and Moving Objects with Commercial Off the Shelf Hardware

Stereo Observation Models

Miniaturized Camera Systems for Microfactories

Index. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 253

Lecture 19: Depth Cameras. Visual Computing Systems CMU , Fall 2013

3D Modeling of Objects Using Laser Scanning

Simple, Accurate, and Robust Projector-Camera Calibration. Supplementary material

AXIOMATIC DESIGN & QFD: A CASE STUDY OF A REVERSE ENGINEERING SYSTEM FOR CUTTING TOOLS

Introduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation

Flexible Calibration of a Portable Structured Light System through Surface Plane

High Altitude Balloon Localization from Photographs

Other Reconstruction Techniques

Miniature faking. In close-up photo, the depth of field is limited.

Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Where does the depth information come from?

Computer Vision Lecture 17

Rectification and Distortion Correction

Computer Vision. Coordinates. Prof. Flávio Cardeal DECOM / CEFET- MG.

Computer Vision Lecture 17

Graphics Systems and Models

Chapters 1 7: Overview

Project 4 Results. Representation. Data. Learning. Zachary, Hung-I, Paul, Emanuel. SIFT and HoG are popular and successful.

LET S FOCUS ON FOCUSING

Mech 296: Vision for Robotic Applications. Today s Summary

Recap from Previous Lecture

Geometry of Aerial photogrammetry. Panu Srestasathiern, PhD. Researcher Geo-Informatics and Space Technology Development Agency (Public Organization)

There are many cues in monocular vision which suggests that vision in stereo starts very early from two similar 2D images. Lets see a few...

Projector Calibration for Pattern Projection Systems

CS 4495/7495 Computer Vision Frank Dellaert, Fall 07. Dense Stereo Some Slides by Forsyth & Ponce, Jim Rehg, Sing Bing Kang

Rectification and Disparity

Improving the 3D Scan Precision of Laser Triangulation

arxiv: v1 [cs.cv] 28 Sep 2018

Jump Stitch Metadata & Depth Maps Version 1.1

Optimized Design of 3D Laser Triangulation Systems

Depth Range Accuracy for Plenoptic Cameras

Fundamentals of Stereo Vision Michael Bleyer LVA Stereo Vision

Public Library, Stereoscopic Looking Room, Chicago, by Phillips, 1923

Chapters 1 9: Overview

DEVELOPMENT OF REAL TIME 3-D MEASUREMENT SYSTEM USING INTENSITY RATIO METHOD

3D-2D Laser Range Finder calibration using a conic based geometry shape

International Society for Photogrammetry and Remote Sensing

And. Modal Analysis. Using. VIC-3D-HS, High Speed 3D Digital Image Correlation System. Indian Institute of Technology New Delhi

Information page for written examinations at Linköping University TER2

Multi-view stereo. Many slides adapted from S. Seitz

Autonomous Vehicle Navigation Using Stereoscopic Imaging

Computer Vision cmput 428/615

Head Mounted Display Optics I!

ENGN D Photography / Spring 2018 / SYLLABUS

SRI Small Vision System

CONTENTS. High-Accuracy Stereo Depth Maps Using Structured Light. Yeojin Yoon

Vision-based Mobile Robot Localization and Mapping using Scale-Invariant Features

BIL Computer Vision Apr 16, 2014

Machine Vision Systems

Computer Vision I. Dense Stereo Correspondences. Anita Sellent 1/15/16

Scalable geometric calibration for multi-view camera arrays

Transcription:

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 or Photogrammetry Time of Flight Structured Light 2

Depth calculated with triangulation Use Triangulation (Geometry) to Determine the Depth of an Object By Different Methods: What do we need to know? Physical location in space Optical Pixel Ray Vectors Matched pixels from feature Identified Feature (X,Y,Z) u v θ u` v` Φ (x,y,z) Viewpoint in Space Triangulation Base (x`,y`,z`) Viewpoint in Space 3

Using Cameras for 3D Machine Vision Two cameras capture different viewpoints of the same object What if object does not have many identifiable features? Difficult to tell different points on this surface (X,Y,Z) u v u` v` (x,y,z) Camera Triangulation Base (x`,y`,z`) Camera 4

3D Machine Vision with Structured Light One camera captures projected patterns The projected patterns inherently create identifiable features (X,Y,Z) Can easily identify all points because of projected pattern u v u` v` (x,y,z) Projector Triangulation Base (x`,y`,z`) Camera 5

What subsystems are needed? Subsystems Calibration Geometry Structured Light Purpose Determine physical locations and directions Determine optical parameters of camera and projector o Focal length o Focal point o Radial distortion coefficients Uses calibration data and feature identification to reconstruct (X,Y,Z) point Generate structured light patterns Decode captured images and generate disparity map which details which projector pixels are viewed by camera DLP Platform Project high-speed patterns Camera Capture high-speed patterns for analysis 6

Process Flow: Calibration Calibration Camera DLP Platform Structured Light Geometry Simultaneous Actions Printed Board Capture Calibration Boards Generate Calibration Data Generate Calibration Boards Project Calibration Board Camera and Projector Calibration Data 7

Process Flow: Scanning Calibration Camera DLP Platform Structured Light Geometry Simultaneous Actions Capture Patterns Decode Patterns Generate Structured Light Patterns Disparity Map Project Patterns 8

Process Flow: Reconstruction Calibration Camera DLP Platform Structured Light Geometry Camera and Projector Calibration Data Reconstruct Point Cloud Point Cloud Disparity Map 9

Connecting the Hardware Connect Camera to USB3.0 port if available Connect DLP LightCrafter 4500 EVM to any USB port Connect Camera trigger cable to DLP LightCrafter 4500 EVM input trigger 10

How to make the calibration board Open application directory and start executable Enter menu item 1: Generate camera calibration board and enter feature measurements 11

How to make the calibration board After selecting menu item 1, a BMP file with the chessboard is generated in the output/calibration_camera directory Print the BMP file (at high DPI) and attach it to a flat surface ¼ Foam core board, aluminum sheet stock, etc. all work well Use spray adhesive to attach printed chessboard Your point cloud data will only be as good as your calibration board! Flatness is critical! 12

Entering calibration board measurements Enter 1 after printing and attaching the flat board Measure and enter the height and width of the calibration pattern Note: Point cloud data units will be in the same units as are entered here 13

Preparing software and projector Preparing the software and projector does the following: Loads calibration and structured light settings Generates projector calibration pattern Generates structured light patterns Uploads images to DLP LightCrafter 4500 EVM The first time you use the projector with the software or change any structured light settings, use option 2: Prepare DLP LightCrafter 4500 (once per projector) Performs all steps listed above If settings have not changed and the projector was previously prepared, use option 3: Prepare system for calibration and scanning Performs all steps above, except uploading images to DLP LightCrafter 4500 EVM Must be run every time the application is run 14

Calibrating the Camera - Setup Before capturing any board positions, set the aperture and focus Aperture determines how much light reaches the sensor Focus ensures the image plane is at the exact level of the sensor so that the image is sharp and not blurry Lock everything into place! Under exposed Over-exposed Good exposure 15

Calibrating the Camera Watch out for Software won t find the chessboard if There is too much glare To remove glare, angle the calibration board Part of the chessboard is missing from within the captured image Parts of the squares on the border square can be cutout, so long as the inside corners are still visible 16

Calibrating the Camera Example Images Calibration image examples Measured camera reprojection error = 0.166341 17

Calibrating the system - Setup Mount the camera so that the projected area can be seen within the camera at the minimum and maximum scanning distance Try to utilize the entire camera frame if possible If the camera or projector are moved relative to each other, this calibration process must be redone Furthest distance Closest distance 18

Calibrating the system Watch out for Software won t find the chessboard if There is too much glare To remove glare, angle the calibration board Part of the projected image falls off of the calibration board This will cause squares to be missing on the projected chessboard calibration pattern 19

Calibrating the System Example Images Calibration image examples Measured projector reprojection error = 0.325859 20

Perform Scan After preparation and calibration, the system is ready for scanning! Use one of the Perform Scan menu options 6, 7, or 8 21

Point Cloud Example 22