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Bundler Structure from Motion Toolkit
| Date | Stars |
|---|---|
| 2026-07-24 | 1576 |
| 2026-07-25 | 1576 |
| 2026-07-28 | 1576 |
| 2026-07-30 | 1576 |
| 2026-08-06 | 1576 |
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Bundler User's Manual --------------------- written by Noah Snavely ([email protected]) based on the Photo Tourism work of Noah Snavely, Steven M. Seitz, (University of Washington) and Richard Szeliski (Microsoft Research) For more information, see the Bundler homepage at http://www.cs.cornell.edu/~snavely/bundler/ or see the FAQ at http://www.cs.cornell.edu/~snavely/bundler/faq.html 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 it may also compile in Windows under Cygwin, and a Visual Studio solution file is also provided). 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. If you use Bundler for a publication, please cite the following paper: Noah Snavely, Steven M. Seitz, and Richard Szeliski. Photo Tourism: Exploring Photo Collections in 3D. SIGGRAPH Conf. Proc., 2006. 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. A utility program for converting bundle files (.out) to the input required by Dr. Yasutaka Furukawa's PMVS multi-view stereo system (http://www.di.ens.fr/pmvs/) called Bundle2PMVS is also included. Finally, this distribution includes a program called RadialUndistort for generating undistorted images (based on the undistortion parameters estimated by Bundler). Before you begin ---------------- You'll first need to download the Bundler distribution from GitHub: or visit the Bundler homepage at http://phototour.cs.washington.edu/bundler 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). You'll also need the 'jhead' program installed, for computing focal lengths from Exif metadata. This is available, for instance, as the jhead package on Ubuntu. Finally, make sure you have the ImageMagick library installed. The utils/bundler.py script requires that you have Python and the Python Image Library (PIL) installed on your computer. To make bundler, just type 'make' in the main bundler directory. Note that if you plan to run Bundler on large problems, you may wish to enable the use of the Ceres solver for bundle adjustment, which can improve speed over the default SBA bundle adjuster. To do so, edit the file 'src/Makefile' and uncomment the line USE_CERES=true Note that this assumes you have Ceres and its dependencies installed on your system. See the Ceres solver page at https://code.google.com/p/ceres-solver/ for more information. Finally, once Bundler is compiled, copy the approximate nearest neighbors (ANN) shared library at BASE_PATH/bin/libANN_char.so (Linux/cygwin) or BASE_PATH/bin/ann
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b9ee07ecaa4298d8, topic:computer-vision, topic:3d-reconstruction, readme:3d reconstruction