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Survey: Visionbased Model generation of 3D real world scene

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Title: Survey: Visionbased Model generation of 3D real world scene


1
Survey Vision-based Model generation of 3-D
real world scene
  • ???, Marc Nguyen, ???
  • Nov. 05

2
Introduction
  • Building 3-D Model without using wrestling with
    CAD tools for months ?
  • Labor-intensive
  • time-consuming
  • resulting models is apparently computer-generated
  • cant be sure about the accuracy of the model
  • The Alternatives
  • vision-based approach
  • take some photos, process them, ready-to-go
  • 3-D scanning
  • not suitable for outdoor scene

3
Modeling Approaches
Geometry(CAD) -based
Vision-based
4
Decisions to make
  • Model polygon or image?
  • Tightly coupled to utilization of the model
  • 3-D polygonal model
  • conventional VR walk-thru / fly-by
  • image-based model
  • image-based renderering/VR (sort of QuickTime
    VR)
  • User input?
  • Fully automatic
  • User input as needed

5
Getting Polygonal models
  • Existing works
  • Depth map textures
  • Hybrid approach Devebec96
  • Issues
  • shape-from
  • Stereo
  • Motion
  • something else ?
  • which feature to use
  • pixel, line, face,

6
Basic PrinciplesStereo Vision(1/5)
  • Basic formula
  • reconstruction of the 3-D coordinates of a number
    of points in a scene for given 2 (or more) images
    obtained by cameras of known relative positions
    and orientations
  • Correspondence problem
  • given a token in image 1, what is the
    corresponding token in image 2?

7
Basic PrinciplesStereo Vision(2/5)
  • Constraints
  • epipolar constraint
  • for a given point in the plane 1, its possible
    matches in the plane 2 all lie on a line,
    therefore search space is reduced from 2D to 1D

8
Basic PrinciplesStereo Vision(3/5)
  • Ordering constraints
  • the orders of tokens in one image is preserved in
    the other image (not true when one token is in
    the forbidden region of the other token)

9
Basic PrinciplesStereo Vision(4/5)
  • Planarity constraint
  • if the surfaces of the objects are planar, there
    exists an analytic transformation from the left
    image coordinates to the right image coordinates.

10
Basic PrinciplesStereo Vision(5/5)
  • Limitation
  • still exist ambiguity
  • the distance between of the two camera must be
    sufficiently small

11
Basic PrinciplesModel-based Vision(1/3)
  • Basic principle
  • to recognize 3D objects, compare a scene model
    (constructed by processing images obtained from
    sensors) against entities in a model database
    (containing a discription of each object the
    system is expected to recognize).

12
Basic PrinciplesModel-based Vision(2/3)
  • Related works
  • Hanson and HendersonHans89
  • the automatic synthesis of a specialized
    recognition scheme, called a strategy tree based
    on CAGD(computer aided geometric design) model.
  • Strategy tree
  • describe the search process used for recognition
    and localization of a particular objects in the
    given scene
  • consist of selected 3D features which satisfy
    system constraints and corroborating evidence
    subtrees which are used in the formation of
    hypothesis.

13
Basic PrinciplesModel-based Vision(3/3)
  • Flynn and Jain Flyn91
  • develop a system which uses 3D object
    descriptions created on a commercial CAD system
  • express in both the industry-standard IGES
    (initial graphics exchange specification) form
    and a polyhedral approximation
  • perform geometric inferencing to obtain a
    relational graph representation of the object
    which can be stored in a database of models for
    object recognition

14
Depth map Textures
  • Not provide polygonal representation
  • need further processing(e.g mesh construction)
  • Need special H/W
  • 3D scanner
  • laser range finder
  • video-rate stereo machine

Http//www.cyberware.com
15
Depth map Texture T.Kanade at CMU (1/3)
  • MBV(Modeling by Videotaping)
  • Walking around the room with camcorder, and get
    the 3-D model of the room and the trajectory of
    camera
  • Based on shape-from-motion
  • factorization technique

Terrain
House
http//www.ius.cs.cmu.edu/IUS/mbvc0/www/modeling.h
tml
16
Related works at CMU (2/3)
  • Z-key
  • generation of depth map in real time using
    special purpose H/W

http//www.cs.cmu.edu/afs/cs/project/stereo-machin
e/www/StereoMachine.html
17
Related works at CMU (3/3)
  • Virtualized Reality
  • create virtual models of real-world events (e.g.
    sports)

http//www.cs.cmu.edu/virtualized-reality/
18
Hybrid approach for architectural scene
  • Modeling and Rendering Architecture from
    Photographs A Hybrid Geometry- and Image-based
    Approach, Proc. SIGGRAPH 96
  • For architectural scene
  • Hierarchy of Block primitives
  • parameter reduction in first phase
  • affordable level in amount of user input
  • Model-based stereo
  • refine rough model to recover the details

SIGGRAPH 96 Conference Proceedings
19
Hybrid approach???, SNU
  • Road and surrouding environments
  • Simplified case of Debevec96
  • Face feature instead of edge

?????????,97??
20
Image-based model
  • Existing works
  • Hirose95
  • View mosaicing
  • Issues
  • how to acquire / store the 2D images ?
  • How to generate seamless image sequence
  • morphing, stitching
  • tightly related to image-based rendering

21
Hirose 95 (1/4)
  • Purpose
  • generation of virtual words by processing 2D real
    images taken by video cameras
  • Basic concept
  • image recording
  • position recording
  • image generation for users viewpoint

Presence, Vol 5, No 1, http//ghidorah.t.u-tokyo.a
c.jp/Projects/IBR/
22
Hirose 95 (2/4)
Image Recording H/W
23
Hirose 95 (3/4)
  • Image synthesis system
  • Search for nearby images in the database
  • Basic operations shift, scale, rotation of
    recorded image
  • Combination of basic operations
  • Enhanced system
  • Use multi-images interpolation
  • Reduce the the feeling of abrupt switch from one
    image to another

24
Hirose 95 (4/4)
  • Advantages of the method
  • easy way to generate virtual worlds
  • very realistic appearance
  • Drawbacks
  • No possibility of users interaction
  • Archiving volume very large
  • Image processing problems (speed, distortion,)
  • Future works
  • Use of new technology for archiving virtual
    worlds
  • Generation of wide virtual worlds (world
    database)
  • evolved into CABIN?

25
View mosaicing
  • process of registering several images to obtain a
    single coherent image
  • Suitable for looking around style VR

http//falcon.postech.ac.kr/people/narziss/image_m
osaic/mosaic.html http//www.cs.cornell.edu/Info/P
eople/kleong/mosaic.html
26
Szeliski96 (1/2)
  • Purpose
  • Set of techniques for building image mosaics
  • Virtual reality applications
  • Planar image mosaics
  • Different pictures are used to generate one wide
    planar image
  • Panoramic image mosaics
  • Set of images taken from one viewpoint with a
    rotation of the camera
  • Panoramic effect -gt illusion of real view and
    scene
  • Used for outdoor scenic view, building interior
    in virtual reality applications

IEEE CGA 1996
27
Szeliski96 (2/2)
  • Projective depth recovery
  • Necessary for illusion of 3D
  • Conclusions
  • These techniques can be used as vision based
    generation of virtual worlds
  • Photorealistic appearance and try to restore 3D
    effect
  • But now only static applications
  • May be used as a part of more complete
    vision-based system

28
Summary
  • Getting realistic model of real world object /
    scene without CAD
  • indoor, human-scale object 3D scanning
  • outdoor scene vision-based approach
  • Based on computer vision techniques
  • human input to some degree might be very helpful
  • Hybrid approach
  • towards moving objects / realtime modeling
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