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NEWS: 9 December 2005

Click >>here<< to download the correct page 121 of the lecture notes.

NEWS: 24 November 2005

Due to serious and unforeseeable family problems, it is likely that Prof. Van Gool will not be able to be in Verona for the School. In that case, Maarten Vergauwen, who was already in charge of half of the course, will give all the lectures.

However, you can withdraw from the School by sending a message to the school secterariat (vips_school@sci.univr.it) by friday 2/12/05, and the fees will be refunded.

We apologize for the inconvenient.

Vittorio Murino
Andrea Fusiello

NEWS: 7 October 2005

The end date of the course is changed: instead of the 9th is the 7th December. Hence the course period will be 5-7 December. As a consequence, the contents written in the bottom will be adapted to the shorter period. We will update the contents as soon as we will know something more precise.


6th VIPS Advanced School on
Computer Vision, Pattern Recognition
and Image Processing

Verona by night from Castel S. Pietro
(click >>here<< for a free tourist guide and infos about Verona)

Click here for a tourist tour!
December 5-7, 2005
Organized by the Vision, Image Processing and Sound Laboratory
Department of Computer Science, University of Verona, Italy

This is the 6th Advanced School organized by the VIPS laboratory, the sixth of a series of advanced lectures on significant topics in Computer Vision, Pattern Recognition, and Image Processing.

These courses are particularly addressed to PhD students, but open to all types of researchers. Each course will typically be held in at most one week and will be focused on one specific topic in order to provide a more productive interaction with the lecturer.

The maximum number of participants is limited to 50 persons. In case of a larger number of applications, priority will be given to PhD students.

This school is titled "Computer Vision Techniques for Passive 3D Acquisition". Details about the course, contents and the registration procedure are given in the following.



The 6th Advanced School is supported by GIRPR, (Gruppo Italiano Ricercatori in Pattern Recognition)


Directors

Vittorio Murino
Andrea Fusiello

Local Organizers

Andrea Colombari
Cheng Dong Seon
Marco Cristani
Davide Moschini


Lecturers

Prof. Luc Van Gool

Department of Information Technology and Electrical Engineering
ETH Zurich, Switzerland

Ir. Maarten Vergauwen

Departement Elektrotechniek
Katholieke Universiteit Leuven, Heverlee (Leuven), Belgium


Course title

Computer Vision Techniques for Passive 3D Acquisition

Contents

  • Introduction on 3D Techniques
    The course starts with an overview of 3D reconstruction techniques. A distinction will be made between active and passive, uni- and multi-directional, manual and automatic techniques.

  • Automatic Passive 3D Reconstruction: Basics
    In the course we will focus on reconstructing 3D models of scenes and objects automatically from images. In order to understand the techniques and algorithms, some basic principles must be explained.

    • What is an image? How is it formed?
    • Camera models:
      • The linear pinhole-model
      • Non-linear distortions (radial, tangential)
      • Transformations between cameras for the special case of planar objects. The difference between Euclidean, metric, affine and projective transformations.
    • Internal Calibration of cameras
      • Intrinsics (and sometimes extrinsics) from known calibration objects (Tsai, . . . )
      • Radial distortion: from calibration object or algorithm which "straightens bended lines"
    • External Calibration of cameras or "pose-estimation" (Grunert's algorithm)
    • The projective world: A projection is written as a multiplication with a 3x4 matrix
    • Principle of passive 3D: The basic stereo-setup. For 3D reconstruction to succeed we need the 2 "c's": calibration and correspondences

  • Relating Images

    • Features As explained in the basics, one of the important prerequisites for 3D reconstruction is the detection of correspondences between images. To facilitate matters we will first search for interesting points in the images which are stable across views and are therefore excellent candidates for matching.
      • Feature extraction: Harris corners; KLT features; Invariant features; Small-versus wide-baseline matching
      • Feature matching: Matching simple features without descriptors: SSD, NCC; Matching invariant features with descriptors; Matching lines
    • The Geometry of Two Images If we want to search for correspondences between two images, there is an underlying geometric structure that can be employed, called epipolar geometry.
      • What is epipolar geometry?
      • How can it be employed?
      • Computing the F-matrix: linearly (8 matches); non-linearly (7 matches); least-squares with more matches; with conditioning
      • Robust matching - dealing with outliers with RANSAC: Description of the algorithm; Some statistical notes on the number of tries; Preemptive RANSAC
      • F-Guided matching of features
      RANSAC is a generic tool, not only to be used for computing F-matrices. Another typical example is the computation of an homography matrix H between two images.
      • How to compute H (another example of RANSAC)h

  • Relating Multiple Views
    Matches between pairs of images can tell us something but not everything about the camera-setup. We will now deal with multiple views.

    • Three views are related by means of a trifocal tensor. Multiple images can be related with other tensors.
    • Projective reconstruction approach: Projective frame initialization; Projective projection matrix estimation with P-RANSAC; Update and initialization of 3D points

  • Self Calibration
    The images have been related to each other in a projective frame. Unfortunately this implies that the resulting reconstruction is only valid up to any projective transformation. This means that many properties (like orthogonality, relative distances, parallelism) are not preserved. We need to upgrade the result from projective to metric, a process called self calibration.

    • The projective ambiguity
    • Upgrading the result means constraints are needed
    • Constraints on the scene
    • Constraints on the camera's intrinsics: The absolute quadric; Writing down constraints; Solving for the quadric means upgrading to metric; Coupled self-calibration if multiple sequences have the same intrinsics.

  • Bundle Adjustment
    Sequential matching of images has an impact on the build-up of errors. In order to distribute the error over all images, a global optimization process is executed on the data which minimizes the total reprojection error of all 3D points in all images, taking into account the camera model and other constraints.

    • What is bundle adjustment?
    • Substitution to limit the size of the system
    • Usage of sparseness of the resulting matrix

  • Model Selection
    At this point we are capable of reconstructing 3D points and cameras from images only. Unfortunately, it happens often that so-called critical motions of the camera or critical surfaces are encountered during recording. The most obvious of these is a (partly) planar scene.

    • Why does computing an F-matrix fail if the scene is dominantly planar?
    • What is an essential matrix?
    • How to compute an essential matrix: From F to E via K; Directly (Nister's algorithm).

    An essential matrix needs the intrinsics. We can recover them from the non-planar parts. How can we find out which part is planar and which isn't?

    • What is GRIC? Relation to Occam's Razor
    • Formula of GRIC. Explanation of the elements
    • F-GRIC, H-GRIC, PPP-GRIC, HH-GRICr

  • Dense Matching
    Camera calibration and sparse 3D point reconstruction is only one part of the story. For convincing 3D models, we must reconstruct much more 3D points, i.e. obtain a dense reconstruction. In order to do so, we must search for dense matches between the images.

    • Standard Stereo: Rectification (homography, or radial polar); Dynamic programming for matching.
    • Matching on the GPU
    • Linking multiple sequential stereo pairs into dense depth maps
    • Multi-View Stereo. A Bayesian approach deals better with occlusions

  • Combining all techniques: 3DWebservice
    The Epoch-webservice combines elements of all previous sections into an automatic 3D reconstruction system.

    • Explanation of the setup
    • Explanation of the server-side
    • Triplet matching and coupled self-calibration
    • Hierarchical method

Possible Extras
If time permits, the topics of the following paragraphs can be discussed.

  • Long Video Sequences
    If we want to reconstruct from very long image sequences (> 1000 frames) we need other techniques.

    • Detection of Key-frames based on GRICs
    • Subsequences for efficient and more stable computation
    • Coupled self-calibration
    • Gluing of subsequences into one frame
    • Computation of intermediate frames if necessary (for 3D TV)

  • Using 3D Techniques in Planetary Exploration

    • Planetary Stereo-Head: Stereo head calibration (pan/tilt); Make use of dominant plane in dense matching to limit disparity; Simulation, 3D viewer and real results.
    • Planetary Aerobots: Windowed bundle-adjustment; Automatic masks for dense matching; JPEG2000 compression; Simulation, 3D viewer and real results.

  • Image-Based Rendering

    • What is IBR?
    • IBR from a set of uncalibrated images?
    • An IBR pipeline constructed for Cultural Heritage: Calibration images of a dome-setup; Using images from a helicopter to extend an existing QTVR object movie.


Final Lectures Schedule

Monday 5 Tuesday 6 Wednesday 7
09.30 - 13.00 09.30 - 13.00
14.30 - 18.30 15.00 - 18.30 15.00 - 16.30


Course Fees

150 euro for PhD and undergraduate students.
200 euro for post doc, researchers, and other people working directly in a university.
300 euro for everybody else.


Registration

If you are interested, you must send an email to vips_school@sci.univr.it in which you ask for participation. Please, state your identity and your status (undergraduate, PhD student, other) and wait for the confirmation email. The ultimate deadline is November 12, 2005.

Attached to our confirmation email you will find a registration form to print, compile and send together with a proof of the payment by fax before November 19, 2005, to the following no. +39 045 8027068, to the attention of Prof. V. Murino, 6th VIPS School on Computer Vision, Pattern Recognition, and Image Processing.

The proposed payment method is bank wire transfer (all necessary data are in the form).


Important Dates

Registration deadline:

November 12, 2005

(E-mail)

Course Fee payment deadline:

November 19, 2005

(Registration form + Proof of payment)

School:

December 5-7, 2005


Accomodations

The accomodation costs are not covered by the Course Fee. However, we have made agreements with some convenient hotels and you can find a list of available places here.
If you wish to take advantage of these opportunities please remember to notify to the hotel that you are attending our school.

Information on how to reach our department are presented in this page.

For any other information, please send an email to vips_school@sci.univr.it

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Last revision: 7 October, 2005