Building an Autostereoscopic Display CS448A – Digital Photography and Image-Based Rendering Billy Chen.

Slides:

Advertisements

Similar presentations

Image Rectification for Stereo Vision

Advertisements

Interactive Deformation of Light Fields Billy Chen Eyal Ofek Heung-Yeung Shum Marc Levoy.

Cameras and Projectors

Multimedia Specification Design and Production 2012 / Semester 1 / week 6 Lecturer: Dr. Nikos Gazepidis

Fourier Slice Photography

Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 

Chap 1 Image fundamental. Trends Image processing techniques have developed from Gray-level processing to color processing 2-D processing to 3-D processing.

Edge Detection CSE P 576 Larry Zitnick

Rendering With Coherent Layers Jed LengyelJohn Snyder Microsoft Research Jed LengyelJohn Snyder Microsoft Research SIGGRAPH 97.

Camera Models A camera is a mapping between the 3D world and a 2D image The principal camera of interest is central projection.

CS485/685 Computer Vision Prof. George Bebis

COMP322/S2000/L221 Relationship between part, camera, and robot (cont’d) the inverse perspective transformation which is dependent on the focal length.

Photorealistic Rendering of Rain Streaks Department of Computer Science Columbia University Kshitiz Garg Shree K. Nayar SIGGRAPH Conference July 2006,

Image Stitching and Panoramas

Image-Based Rendering using Hardware Accelerated Dynamic Textures Keith Yerex Dana Cobzas Martin Jagersand.

What’s on page 13-25? Tom Butkiewicz. Refresh Rates Flicker from shutter systems Halve refresh rates 2 eyed 120Hz != 1 eyed 60Hz Phosphors 2 Polarized.

Projection Defocus Analysis for Scene Capture and Image Display Li Zhang Shree Nayar Columbia University IIS SIGGRAPH Conference July 2006, Boston,

Graphics Systems I-Chen Lin’s CG slides, Doug James’s CG slides Angel, Interactive Computer Graphics, Chap 1 Introduction to Graphics Pipeline.

CV: 3D sensing and calibration

 Marc Levoy Synthetic aperture confocal imaging Marc Levoy Billy Chen Vaibhav Vaish Mark Horowitz Ian McDowall Mark Bolas.

1 Chapter 5 Viewing. 2 Perspective Projection 3 Parallel Projection.

Integral Photography A Method for Implementing 3D Video Systems.

Today: Calibration What are the camera parameters?

3D Graphics Goal: To produce 2D images of a mathematically described 3D environment Issues: –Describing the environment: Modeling (mostly later) –Computing.

Multi-Aperture Photography Paul Green – MIT CSAIL Wenyang Sun – MERL Wojciech Matusik – MERL Frédo Durand – MIT CSAIL.

Light Field. Modeling a desktop Image Based Rendering  Fast Realistic Rendering without 3D models.

Image formation & Geometrical Transforms Francisco Gómez J MMS U. Central y UJTL.

Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos VC 14/15 – TP3 Digital Images Miguel Tavares Coimbra.

Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.

3D Visualisation of Simulation Data. Informal Seminar 08/03/2004. By Chris Sweet.

Computer Graphics (fall 2009)

01/28/05© 2005 University of Wisconsin Last Time Improving Monte Carlo Efficiency.

Automatic Registration of Color Images to 3D Geometry Computer Graphics International 2009 Yunzhen Li and Kok-Lim Low School of Computing National University.

Dynamically Reparameterized Light Fields Aaron Isaksen, Leonard McMillan (MIT), Steven Gortler (Harvard) Siggraph 2000 Presented by Orion Sky Lawlor cs497yzy.

Sebastian Enrique Columbia University Relighting Framework COMS 6160 – Real-Time High Quality Rendering Nov 3 rd, 2004.

CS559: Computer Graphics Lecture 9: Projection Li Zhang Spring 2008.

3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: , Perspective Geometry Camera Model Stereo Triangulation 3D Reconstruction by.

Image stitching Digital Visual Effects Yung-Yu Chuang with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac.

Image-based Rendering. © 2002 James K. Hahn2 Image-based Rendering Usually based on 2-D imagesUsually based on 2-D images Pre-calculationPre-calculation.

Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.

Stereo 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 1image.

Perspective Shadow Maps Marc Stamminger REVES/INRIA, Sophia-Antipolis, France now at: Bauhaus-Universität, Weimar, Germany George Drettakis REVES/INRIA,

How to startpage 1. How to start How to specify the task How to get a good image.

Computer Vision : CISC 4/689 Going Back a little Cameras.ppt.

Advanced Computer Graphics Shadow Techniques CO2409 Computer Graphics Week 20.

112/5/ :54 Graphics II Image Based Rendering Session 11.

Review on Graphics Basics. Outline Polygon rendering pipeline Affine transformations Projective transformations Lighting and shading From vertices to.

 Marc Levoy Using Plane + Parallax to Calibrate Dense Camera Arrays Vaibhav Vaish, Bennett Wilburn, Neel Joshi, Marc Levoy Computer Science Department.

 Vaibhav Vaish Synthetic Aperture Focusing Using Dense Camera Arrays Vaibhav Vaish Computer Graphics Laboratory Stanford University.

Intelligent Vision Systems Image Geometry and Acquisition ENT 496 Ms. HEMA C.R. Lecture 2.

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

Digital Image Processing Additional Material : Imaging Geometry 11 September 2006 Digital Image Processing Additional Material : Imaging Geometry 11 September.

CS559: Computer Graphics Lecture 9: 3D Transformation and Projection Li Zhang Spring 2010 Most slides borrowed from Yungyu ChuangYungyu Chuang.

Robotics Chapter 6 – Machine Vision Dr. Amit Goradia.

Digital Image Processing Image Enhancement in Spatial Domain

Auto-stereoscopic Light-Field Display By: Jesus Caban George Landon.

Image Warping 2D Geometric Transformations

3D Rendering 2016, Fall.

Rendering Pipeline Fall, 2015.

CS4670 / 5670: Computer Vision Kavita Bala Lecture 20: Panoramas.

COSC579: Image Align, Mosaic, Stitch

CSCE 441 Computer Graphics 3-D Viewing

CSC461: Lecture 20 Parallel Projections in OpenGL

Sampling and Reconstruction of Visual Appearance

© University of Wisconsin, CS559 Fall 2004

Coding Approaches for End-to-End 3D TV Systems

Idea: projecting images onto a common plane

(c) 2002 University of Wisconsin, CS 559

Camera Calibration Coordinate change Translation P’ = P – O

Presentation transcript:

Building an Autostereoscopic Display CS448A – Digital Photography and Image-Based Rendering Billy Chen

Original Goals dynamic, real-time display convenient 3D display for the home (3D desktops) autostereoscopic light field viewer

Display design choices Display TypeResolutionAdvantageDisadvantage CRT/LCD dpicheap, everywherelow resolution, challenging calibration Projector~150 dpieasier to control pixel depth, adjustable angular resolution projector distortion Big Bertha dpihigher resolutionexpensive, need special hardware to drive it Printer300 dpihigh resolutionstatic images

Physical Setup

Overview of display process Render ` Calibration

The calibration problem +

Calibration affine transformation correction (mostly scale) projective transformation correction

Calibration solution 1 OpenGL program displays a moiré pattern can calibrate up to affine transformations most effective for finding correct size

Calibration solution 2 p h p Finding the homography without getting projector parameters A B A’ B’

xy1xy1 cx’ cy’ c Calibration solution 2 M = M p cp’ Let M i = i’th row of M (1) M 1 p = cx’ (2) M 2 p = cy’ (3) M 3 p = c y’ (M 1 p) - x’(M 2 p) = 0 M 1 p - x’(M 3 p) = 0 xy’ yy’ y’ -xx’ -yx’ -x’ M 11 M 12 M 13 M = 0 8x9 9x1 A= Take SVD(A) and look at matrix

Calibration solution 2

Rendering sampling the light field computing lenslet distances cropping and compositing

Rendering: Sampling a light field Isaksen et al., Siggraph 2000

Getting “floating” images Halle, Kropp. SPIE ‘97

Sampling a light field

Rendering: computing the FOV

Rendering: compositing and cropping images subsample crop composite

Implementation Details Fresnel hex array #300; 0.12 in. focal length, 0.12 in. thickness,.09 in. diameter default size for a lenslet image: 26x31 pixels (for 300 dpi displays) calibrate scale is.49 (sanity check: 300 dpi / 150 dpi) OpenGL unit == 1 pixel (300 dpi) SEE WEBPAGE!

Results compared to original goals real-time display is hard, must handle the bandwidth spatial resolution too small for 3D desktops light fields have problems with much depth complexity, but NEED depth for effective autostereoscopic displays

Future Work reflective display auto-calibration hardware accelerated light field sampling overloading pixels per direction: perspective views, displacing display pixels from focal plane use a light field of captured data

Acknowledgements calibration: Vaibhav Vaish light field generator: Georg Petschnigg hardware accelerated approach: Ren Ng bootstrap: Sean Anderson