--------------------------------------------------------------------------
  Bundler v0.1 User's Manual
    copyright 2008 Noah Snavely (snavely@cs.washington.edu)
  
  based on the Photo Tourism work of Noah Snavely, Steven M. Seitz, 
    (University of Washington) and Richard Szeliski (Microsoft Research)

  For more technical information, visit http://phototour.cs.washington.edu

  An html version of this manual is available at 
     http://phototour.cs.washington.edu/bundler/
--------------------------------------------------------------------------


----------------
What is Bundler?
----------------

Bundler is a structure-from-motion system for unordered image
collections (for instance, images from the Internet). Bundler takes a
set of images, image features, and image matches as input, and
produces a 3D reconstruction of the camera and (sparse) scene geometry
as output. The system, described in [1] and [2], reconstructs the
scene incrementally, a few images at a time, using a modified version
of the Sparse Bundle Adjustment package of Lourakis and Argyros [3] as
the underlying optimization engine.

Currently, Bundler has been primarily compiled and tested under Linux
(though a Windows version for cygwin has also been released).


-----------------
Conditions of use
-----------------

Bundler is distributed under the GNU General Public License.  For
information on commercial licensing, please contact the authors at the
contact address below.


---------------
What's included
---------------

Included with the binary distribution is the Bundler executable
(bin/bundler), as well as a number of other utility scripts and
executables (in the bin/ directory). In addition, there are a number
of example image sets (and example results) under the examples/
directory. A version of the approximate nearest neighbors (ANN)
library of David M. Mount and Sunil Arya, customized for searching
verctors of unsigned bytes, is also included.

Finally, included in the bin directory is the 'jhead' program for
reading Exif tags from JPEG images. Very special thanks to Matthias
Wandel for putting this useful program in the public domain.


----------------
Before you begin
----------------

You'll first need to download the Bundler distribution from:

http://phototour.cs.washington.edu/bundler 
(the binary distribution is highly recommended)

and extract it into a directory (to be referred to as BASE_PATH).

You'll also need a feature detector components to get the system
working. Assuming you will be using SIFT features generated by David
Lowe's SIFT binary, you'll need to download that binary from

    http://www.cs.ubc.ca/~lowe/keypoints/

and copy it to BASE_PATH/bin (making sure it is called 'sift', or
'siftWin32.exe' under Windows).

Next, you'll need to modify three scripts to set the BASE_PATH
location. The scripts are BASE_PATH/RunBundler.sh,
BASE_PATH/bin/ToSift.sh, and BASE_PATH/bin/extract_focal.pl.

Finally, copy the approximate nearest neighbors (ANN) shared library
at BASE_PATH/lib/libANN_char.so to a location in your LD_LIBRARY_PATH
(or add BASE_PATH/lib to LD_LIBRARY_PATH. 


---------------
Running bundler
---------------

The easiest way to start using Bundler is to use the included bash
shell script, RunBundler.sh.  Just execute this script in a directory
with a set of images in JPEG format, and it will automatically run all
the steps needed to run structure from motion on the images (assuming
everything goes well).  As mentioned above, you'll first need to edit
this script to set the BASE_PATH variable appropriately (again, you'll
also need to edit set the BASE_PATH variable in the Perl script
BASE_PATH/bin/extract_focal.pl and the bash script
BASE_PATH/bin/ToSift.sh).  To test this script, try running it from
one of the example directories (e.g. examples/ET/before).

The Bundler exectutable is actually the last in a sequence of steps
that need to be run to reconstruct a scene.  RunBundler.sh takes care
of all these steps for you, but it's useful to know what's going on.
The main initial steps are to generate features and pairwise feature
matches for the image set.  Any type of image features can be used,
but Bundler assumes the features are in the SIFT format, and so David
Lowe's SIFT detector, available at
http://www.cs.ubc.ca/~lowe/keypoints/, is probably the easiest to get
working with Bundler (RunBundler.sh assumes that SIFT is used).  A
list of images containing estimating focal length information also
must be created.  The four steps to creating a reconstruction are
therefore:

1. Create a list of images using the script 'extract_focal.pl'
   (this extracts focal length information, when available, from each
   image, and stores it in an image list).
2. Generate (SIFT) features for each image.
3. Match features between each pairs of images (this step can take a
   while).  The computed feature matches are stored in a file called
   'matches.init.txt'.
4. Run 'bundler' with a suitable options file.

Again, running the RunBundler.sh script is the easiest way to perform
these steps.  To simplify steps 1-3, a number of utility scripts and
programs are included with this distribution (and invoked from the
RunBundler.sh script).

Bundler itself is typically invoked as follows:

 > bundler list.txt --options_file options.txt

The first argument is the list of images to be reconstructed (created
with the 'create_image_list.pl' utility).  Next, an options file
containing settings to be used for the current run is given.
RunBundler.sh creates an options file that will work in many
situations.  Common options are described later in this document.


-------------
Output format
-------------

Bundler produces files typically called 'bundle_*.out' (we'll call
these "bundle files").  With the default commands, Bundler outputs a
bundle file called 'bundle_<n>.out' containing the current state of
the scene after each set of images has been registered (n = the number
of currently registered cameras).  After all possible images have been
registered, Bundler outputs a final file named 'bundle.out'.  In
addition, a "ply" file containing the reconstructed cameras and points
is written after each round.  These ply files can be viewed with the
"scanalyze" mesh viewer, available at
http://graphics.stanford.edu/software/scanalyze/.

The bundle files contain the estimated scene and camera geometry have
the following format:

<num_cameras> <num_points>   [two integers]
<camera1>
<camera2>
...
<cameraN>
<point1>
<point2>
...
<pointM>

Each camera entry <cameraI> contains the estimated camera intrinsics
and extrinsics, and has the form:

<f> <k1> <k2>   [the focal length, followed by two radial distortion coeffs]
<R>             [a 3x3 matrix representing the camera rotation]
<t>             [a 3-vector describing the camera translation]

The cameras are specified in the order they appear in the list of
images.

Each point entry <pointI> has the form:
<position>      [a 3-vector describing the 3D position of the point]
<color>         [a 3-vector describing the RGB color of the point]
<view list>     [a list of views the point is visible in]

The view list begins with the length of the list (i.e., the number of
cameras the point is visible in).  The list is then given as a list of
quadruplets <camera> <key> <x> <y>, where <camera> is a camera index,
<key> the index of the SIFT keypoint where the point was detected in
that camera, and <x> and <y> are the detected positions of that
keypoint.  Both indices are 0-based (e.g., if camera 0 appears in the
list, this corresponds to the first camera in the scene file and the
first image in "list.txt").  

We use a pinhole camera model; the parameters we estimate for each
camera are a focal length (f), two radial distortion parameters (k1
and k2), a rotation (R), and translation (t), as described in the file
specification above.  The formula for projecting a 3D point X into a
camera (R, t, f) is:

    P = R * X + t       (conversion from world to camera coordinates)
    p = -P / P.z        (perspective division)
    p' = f * r(p) * p   (conversion to pixel coordinates)

where P.z is the third coordinate of P.  In the last equation, r(p) is
a function that computes a scaling factor to undo the radial
distortion:

r(p) = 1.0 + k1 * ||p||^2 + k2 * ||p||^4.

This gives a projection in pixels, where the origin of the image is
the center of the image, the positive x-axis points right, and the
positive y-axis points up (in addition, in the camera coordinate
system, the positive z-axis points backwards, so the camera is looking
down the negative z-axis, as in OpenGL).

Finally, the equations above imply that the camera viewing direction
is:

    R' * [0 0 -1]'  (i.e., the third row of R or third column of R')

(where ' indicates the transpose of a matrix or vector).

and the 3D position of a camera is 

    -R' * t .


--------------------
Command-line options
--------------------

Bundler has a number of internal parameters, so there are a large
number of command-line options.  That said, we've found that a common
set of parameters works well for most image collections we've tried,
so it is probably safe to start with the recommended options (used by
the RunBundler.sh script).  One very useful option is 
"--options_file <file>", which tells Bundler to read a list of options
from a file.  The default options file created by RunBundler.sh
includes the following:

  --match_table matches.init.txt
     [specifies the file where the match files are stored]

  --output bundle.out
     [specifies the name of the final output reconstruction]

  --output_all bundle_
     [specifies that all intermediate reconstructions should be
      output to files with prefix "bundle_"]

  --output_dir bundle
     [the directory all output files should be written to, typically
      called "bundle"]

  --variable_focal_length
     [directs bundler to optimize for an independent focal length for
      each image]

  --use_focal_estimate
     [directs bundler to use the estimated focal lengths obtained from
      the Exif tags for each image]

  --constrain_focal
     [constrain the focal length of each camera to be close to the
      initial focal length estimate (from Exif tags).  This option
      adds penalty terms to the bundle adjustment objective function]

  --constrain_focal_weight 0.0001
     [weight on the penalty terms for the focal length constraints (a
      small weight is typically sufficient)]

  --estimate_distortion
     [directs bundler to estimate radial distortion parameters for
      each image]

  --run_bundle
     [run structure from motion (as opposed to other operations on
      existing reconstructions)]

There are a number of other useful options in addition to the default
ones listed above, including:

  --init_pair1 <image_idx1>
  --init_pair2 <image_idx2>
     [Specifies which images to use as the initial pair.  Very useful
      when the automatically chosen pair results in a bad
      reconstruction.]

  --options_file <options_file>
     [Read in a list of options from the specified file.]

  --help
     [Print out the complete list of command-line options.]


----------------
Acknowledgements
----------------

This work was supported by Microsoft Research, the University of
Washington Animation Research Labs, an Achievement Rewards for College
Scientists (ARCS) fellowship, National Science Foundation grants
IIS-0413198 and DGE-0203031, and an endowment by Emer Dooley and Rob
Short.

Thanks to Manolis Lourakis and Antonis Argyros for their sparse bundle
adjustment package (http://www.ics.forth.gr/~lourakis/sba/), to David
Lowe for SIFT (http://www.cs.ubc.ca/~lowe/keypoints/), to David
M. Mount and Sunil Arya for their approximate nearest neighbors
library (http://www.cs.umd.edu/~mount/ANN/), and to Matthias Wandel
for his 'jhead' program.

Thanks as well to Kathleen Tuite and Sebastian Koch for testing this
distribution.


-------------------
Contact information
-------------------

Questions?  Comments?  Bug reports?  Please see the FAQ at <TODO>, or
send email to Noah Snavely at snavely (at) cs.washington.edu.


[1] Noah Snavely, Steven M. Seitz, and Richard Szeliski.  Photo
    Tourism: Exploring Photo Collections in 3D.  SIGGRAPH Conf. Proc.,
    2006.

[2] Noah Snavely, Steven M. Seitz, Richard Szeliski.  Modeling the
    World from Internet Photo Collections. International Journal of
    Computer Vision (to appear), 2007.

[3] M.I.A. Lourakis and A.A. Argyros.  The Design and Implementation
    of a Generic Sparse Bundle Adjustment Software Package Based on
    the Levenberg-Marquardt Algorithm.  Tech.  Rep. 340, Inst. of
    Computer Science-FORTH, Heraklion, Crete, Greece. Available from
    http://www.ics.forth.gr/~lourakis/sba.
