Dynamic View Morphing performs view interpolation of dynamic scenes.

Slides:



Advertisements
Similar presentations
Epipolar Geometry.
Advertisements

More on single-view geometry
Virtual Realism TEXTURE MAPPING. The Quest for Visual Realism.
3D reconstruction.
EVENTS: INRIA Work Review Nov 18 th, Madrid.
Dr. Hassan Foroosh Dept. of Computer Science UCF
View Morphing (Seitz & Dyer, SIGGRAPH 96)
Camera calibration and epipolar geometry
Geometric Transformation & Projective Geometry
Structure from motion.
Structure from motion. Multiple-view geometry questions Scene geometry (structure): Given 2D point matches in two or more images, where are the corresponding.
Uncalibrated Geometry & Stratification Sastry and Yang
CS485/685 Computer Vision Prof. George Bebis
Projective geometry Slides from Steve Seitz and Daniel DeMenthon
3-D Geometry.
Multiple-view Reconstruction from Points and Lines
3D reconstruction of cameras and structure x i = PX i x’ i = P’X i.
1Jana Kosecka, CS 223b Cylindrical panoramas Cylindrical panoramas with some slides from R. Szeliski, S. Seitz, D. Lowe, A. Efros,
Grading for ELE 5450 Assignment 28% Short test 12% Project 60%
1 Lecture 11 Scene Modeling. 2 Multiple Orthographic Views The easiest way is to project the scene with parallel orthographic projections. Fast rendering.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
CV: 3D sensing and calibration
MSU CSE 803 Fall 2008 Stockman1 CV: 3D sensing and calibration Coordinate system changes; perspective transformation; Stereo and structured light.
COMP 290 Computer Vision - Spring Motion II - Estimation of Motion field / 3-D construction from motion Yongjik Kim.
Lec 21: Fundamental Matrix
Stockman MSU/CSE Math models 3D to 2D Affine transformations in 3D; Projections 3D to 2D; Derivation of camera matrix form.
3D Computer Vision and Video Computing 3D Vision Topic 8 of Part 2 Visual Motion (II) CSC I6716 Spring 2004 Zhigang Zhu, NAC 8/203A
Geometric Objects and Transformations Geometric Entities Representation vs. Reference System Geometric ADT (Abstract Data Types)
3-D Scene u u’u’ Study the mathematical relations between corresponding image points. “Corresponding” means originated from the same 3D point. Objective.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Multi-view geometry. Multi-view geometry problems Structure: Given projections of the same 3D point in two or more images, compute the 3D coordinates.
Automatic Camera Calibration
Shape Blending Joshua Filliater December 15, 2000.
Technology and Historical Overview. Introduction to 3d Computer Graphics  3D computer graphics is the science, study, and method of projecting a mathematical.
Brief Introduction to Geometry and Vision
Geometric Models & Camera Calibration
Week 5 - Wednesday.  What did we talk about last time?  Project 2  Normal transforms  Euler angles  Quaternions.
2 COEN Computer Graphics I Evening’s Goals n Discuss the mathematical transformations that are utilized for computer graphics projection viewing.
CS654: Digital Image Analysis Lecture 8: Stereo Imaging.
Metrology 1.Perspective distortion. 2.Depth is lost.
Single View Geometry Course web page: vision.cis.udel.edu/cv April 9, 2003  Lecture 20.
Motion Analysis using Optical flow CIS750 Presentation Student: Wan Wang Prof: Longin Jan Latecki Spring 2003 CIS Dept of Temple.
Affine Structure from Motion
OpenGL The Viewing Pipeline: Definition: a series of operations that are applied to the OpenGL matrices, in order to create a 2D representation from 3D.
112/5/ :54 Graphics II Image Based Rendering Session 11.
Two-view geometry. Epipolar Plane – plane containing baseline (1D family) Epipoles = intersections of baseline with image planes = projections of the.
EECS 274 Computer Vision Affine Structure from Motion.
Computer Graphics Lecture 08 Fasih ur Rehman. Last Class Ray Tracing.
stereo Outline : Remind class of 3d geometry Introduction
Review on Graphics Basics. Outline Polygon rendering pipeline Affine transformations Projective transformations Lighting and shading From vertices to.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Computer vision: models, learning and inference M Ahad Multiple Cameras
Computer Graphics Matrices
Auto-calibration we have just calibrated using a calibration object –another calibration object is the Tsai grid of Figure 7.1 on HZ182, which can be used.
MASKS © 2004 Invitation to 3D vision Uncalibrated Camera Chapter 6 Reconstruction from Two Uncalibrated Views Modified by L A Rønningen Oct 2008.
Uncalibrated reconstruction Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration.
Structure from Motion Paul Heckbert, Nov , Image-Based Modeling and Rendering.
Image-Based Rendering Geometry and light interaction may be difficult and expensive to model –Think of how hard radiosity is –Imagine the complexity of.
Introduction To IBR Ying Wu. View Morphing Seitz & Dyer SIGGRAPH’96 Synthesize images in transition of two views based on two images No 3D shape is required.
Homographies and Mosaics : Computational Photography Alexei Efros, CMU, Fall 2006 © Jeffrey Martin (jeffrey-martin.com) with a lot of slides stolen.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry
Dynamic View Morphing performs view interpolation of dynamic scenes.
PERSPECTIVE PROJECTION…...
CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.
Modeling 101 For the moment assume that all geometry consists of points, lines and faces Line: A segment between two endpoints Face: A planar area bounded.
3D Graphics Rendering PPT By Ricardo Veguilla.
3D reconstruction class 11
Uncalibrated Geometry & Stratification
Video Compass Jana Kosecka and Wei Zhang George Mason University
Multi-view geometry.
Presentation transcript:

Dynamic View Morphing performs view interpolation of dynamic scenes

Expanded Theory orthography methods for finding camera-to-camera transformation virtual camera not restricted to line connecting original cameras “weak rectification” is sufficient for physical realism appearance of straight-line motion without camera-to-camera transformation

motion from time=0 to time=1, as seen through A

For Orthographic Projection physically correct straight-line motion constant-velocity motion (because motion vectors aligned) (because motion vectors identical)

For Perspective Projection IF first make image planes parallel to: –motion of object, and –each other THEN orthographic results apply condition above is “weak rectification”

A time = 0 B time = 1 camera views related by fundamental matrix F

A B time = 1 time = 0 camera views still related by same fundamental matrix F

A time = 0 B time = 1

A B each object  has its own fundamental matrix F 

denoted T AB once known, view interpolations portray “constant velocity” motion potential for model building Camera-to-camera transformation

Finding T AB can be determined from fundamental matrices for two distinct objects can be determined from four conjugate directions can be approximated from two conjugate directions

Layering Static Objects static “table, walls, and floor” object gets broken into two pieces improves sense of object rigidity

Environment Map Morphing time=0.0 time=0.4 time=1.0

Environment Map “environment map” or “panoramic mosaic” or “plenoptic function”: all the light that reaches a given point in space at an instant in time

Environment Map Morphing View morphing of entire environment maps –uncalibrated cameras –sparse correspondences –widely separated views In particular, view morphing with –camera moving towards scene –object’s vanishing point in view

Interpolating Augmented Views

Benefits placing synthetic object over real object –segmentation –point correspondences –camera-to-camera transformation –added realism: moving parts, shadows, transparency, don’t morph synthetic object –can also use real object views instead of a synthetic object

Benefits automation –by matching edges, computer can place model automatically –all previous benefits become automated scenario visualization –combine synthetic objects with real scenes to create new scenarios

DONE

Layering Static Objects greatly improves sense of object solidity static “table, walls, and floor” object gets broken into two pieces

A B each object  has its own fundamental matrix F 

Environment Map Morphing view morphing for environment maps time=0.0 time=0.4 time=1.0

Analogous to View Morphing rectify image planes interpolate conjugate points use interpolated points to guide morphing algorithm rectify image cylinders interpolate conjugate points use interpolated points to guide morphing algorithm View MorphingEnvironment Map Morphing

interpolate conjugate points Morph* based on interpolated points locate conjugate points rectify image planesrectify image cylinders view morphingenvironment map morphing *cylinder-based morph needed for environment maps

z = 1 “image plane” y 2 + z 2 = 1 “image cylinder”

Environment Map Morphing (STEP 1) find fundamental matrix (STEP 2) “strongly rectify” the views then notice that, for any point in space, camera A and camera B will give the same y and z coordinates that is, make T BA = a b c

Environment Map Morphing (STEP 3) project environment map onto “image cylinder” (a.k.a “pipe”) (STEP 4) interpolate conjugate points and morph this is the cylinder y 2 + z 2 = 1

cylinder y 2 + z 2 = 1

A and B after applying T BA A B = T BA x

Outline layering; static scenes, improvement orthography generalization of math for view morphing making objects appear to follow line Tab and how to find

Underlying Mathematics “weak” rectification: image planes parallel virtual movement not restricted to line

Orthography long-distance photography no prewarps needed! (physical correctness) straight-line motion by aligning directions

Preconditions/Output

Appearance of Straight-line Motion

Orthographic Projection physically correct straight-line motion constant-velocity motion AB

B = T BA x A

A and B t = 1 t = 0 B took this view A took this view after applying T BA

physically correct straight-line motion constant-velocity motion AB