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SfM Init User's Manual

copyright 2012-2014 Kyle Wilson ([email protected])

based on the global structure from motion work with Noah Snavely

Introduction:

SfM Init is a toolkit for solving some parts of a global Stucture from Motion pipeline. Such a pipeline would typically reconstruct the 3D (sparse) geometry of some scene given many photos with the following steps:

  1. Feature Detection
  2. Feature Matching
  3. Two View Model Estimation
  4. Solve for Globally Consistant Camera rotations (wrappers included)
  5. Solve for Globally Consistant Camera positions (included)
  6. Refine the Model through Bundle Adjustment

SfM Init goes from pairwise geometry to a good guess of global geometry, which is then the initialization to bundle adjustment. SfM Init does not ship with a system for computing pairwise models or with a bundle adjustor.

SfM Init uses the excellent Rotations Graph Averaging package by Chatterjee and Govindu [2]. This is available from their project webpage. SfM Init only provides wrappers to call this code.

Conditions of Use

SfM Init is distributed under a Simplified BSD / 2-clause license. If you use SfM Init for a publication, please cite the following paper:

Kyle Wilson and Noah Snavely. Robust Global Translations with 1DSfM. ECCV 2014.

What's Included

SfM Init is distributed as a python package. Some of the key computations are written in C++ and are wrapped in via cython. This should be transparent to the user.

This toolkit includes a wrapper to Chatterjee and Govindu's rotations averaging code, as well 1DSfM translations problem outlier detection and a chordal distance based translations solver.

Examples of how to use SfM Init are in the scripts directory. In particular, scripts/eccv_demo.py shows how to run all the steps of the pipeline described in [1] on the datasets provided at the 1DSfM project page.

Before You Begin

SfM Init is python based, and depends on the following standard python packages --- python 2.7, numpy, and scipy. Additionally, to compile the C++ numeric routines, it requires cython.

The translations solver requires the Ceres Solver nonlinear least squares package.

Chatterjee and Govindu's rotations averaging code can be found at their project page. Unzip the contents of this tar file into the rotsolver directory.

Finally, to compile the numerics rountines, run the following from SfM Init's root directory:

> python setup.py build_ext --inplace

If this fails, check the compile and link paths in setup.py to be sure that cython can see the Ceres include and lib files, as well as Eigen and the SuiteSparse libs that Ceres depends on.

File Formats

Running the SfM Init pipeline in scripts/eccv_demo.py requires several files describing reconstructed two view models. Our datasets are available on our project page. Note that the photos and data files are distributed separately. The dataset files describe a single connected component, but all images related to each Landmark are given, in case these are useful in another context.

Input files:

  • cc.txt: This is a list of camera indices, one per line, specifying which images to reconstruct. These form a single connected component of EGs.
  • EGs.txt: Two-image models are listed in this file, one per line. The format is: <i> <j> <Rij> <tij> where i and j are camera indices, Rij is a row-major pairwise rotation matrix, and t is the position of camera j in camera i's coordinate system. If Ri and Rj are the rotation matrices of cameras i and j (world-to-camera maps) then in the absence of noise Rij = Ri * Rj', ie Rij is the pose of camera j in camera i's coordinate system (where a pose is the transpose of a rotation matrix, a camera-to-world map). All of these EGs are within the connected component.
  • coords.txt: This is a summary of the local image features found in each image in the connected component. Each image starts with a header, followed by a row for each key in that image. The header contains the number of keys in the image, the focal length in pixels, and the principal points (half the width and height). Keys are given as <key number> <x> <y> <ignore0> <ignore1> <R> <G> <B> where R,G,B are a sampled rgb color. The keys are numbered sequentially.
  • tracks.txt: This describes which features in the images in the connected component have been matched into a track. The first line is the number of tracks, and then each following line is a single track with format: N <img1> <feature1> ... <imgN> <featureN>

Output formats:

  • prob.txt: SfM Init reads and writes translations problems as edge lists. A translations problem file has the format: <i> <j> <tij> where tij is a unit vector pointing from node i to node j.
  • soln.txt: SfM Init reads and writes solutions to translations problems as a vertex list. Each line is <i> <Xi> where Xi is a 3-vector.
  • rots.txt: SfM Init reads and writes global rotations solutions as a vertex list: <i> <Ri>, where Ri is a 3-by-3 rotation matrix written row major.

Other included files:

  • list.txt: a list of all of the images in a dataset, as well as image focal lengths in pixels. The format per line is <image name> 0 <focal length>, although when the focal length is unknown the latter two fields are omitted. SfM Init ignores photos with unknown focal length. The line number of an image in this file is its identifying index in the rest of the toolkit. (Note that this list typically includes many more images than are in connected component supplied above.)
  • bundle.out: This is a reconstruction of approximately the same component of the dataset which is described by the other files. Do to differences in the reconstruction method, it may have a few extra images, or fail to reconstruct some images in the connected component. This reconstruction is made with [3], and is provided for comparison purposes. See the bundler manual for details about the file format: http://www.cs.cornell.edu/~snavely/bundler

Contact

Please email Kyle Wilson ([email protected]) with any questions, comments, or bug reports.

References

[1] Kyle Wilson and Noah Snavely. Robust Global Translations with 1DSfM. ECCV 2014.

[2] Avishek Chatterjee and Venu Madhav Govindu. Efficient and Robust Large-Scale Rotation Averaging. ICCV 2013.

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