# SfM-Toy-Library

**Repository Path**: denbianh/SfM-Toy-Library

## Basic Information

- **Project Name**: SfM-Toy-Library
- **Description**: A toy library for Structure from Motion using OpenCV
- **Primary Language**: C++
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Toy Structure From Motion Library using OpenCV

This is a reference implementation of a Structure-from-Motion pipeline in OpenCV, following the work of Snavely et al. [2] and Hartley and Zisserman [1].

*Note:* This is not a complete and robust SfM pipeline implementation. The purpose of this code is to serve as a tutorial and reference for OpenCV users and a soft intro to SfM in OpenCV. If you are looking for a more complete solution with many more options and parameters to tweak, check out the following:

 * OpenMVG http://openmvg.readthedocs.io/en/latest/#
 * libMV https://developer.blender.org/tag/libmv/
 * VisualSFM http://ccwu.me/vsfm/
 * Bundler http://www.cs.cornell.edu/~snavely/bundler/

SfM-Toy-Library is now using OpenCV 3, which introduced many new convenience functions to Structure from Motion (see my [blog post](http://www.morethantechnical.com/2016/10/17/structure-from-motion-toy-lib-upgrades-to-opencv-3/) for details), making the implementation much cleaner and simpler. 

Ceres solver was chosen to do bundle adjustment, for its simple API, straightforward modeling of the problem and long-term support.

Doxygen-style documentation comments appear throughout.

## Compile

To compile use CMake: http://www.cmake.org

### Prerequisite
- OpenCV 3.x: http://www.opencv.org
- Ceres Solver (for bundle adjustment): http://ceres-solver.org/
- Boost C++ libraries v1.54+: http://www.boost.org/

### How to make

#### On OSX Using XCode

Get Boost and Ceres using homebrew: `brew install boost ceres-solver` (you will need to tap `homebrew/science` for Ceres)

	mkdir build
	cd build
	cmake -G "Xcode" ..
	open SfMToyExample.xcodeproj
	
#### On Linux (or OSX) via a Makefile

Obtain Boost (with e.g. `apt-get install libboost-all-dev`) and Ceres (probably need to clone and compile), or on OSX view homebrew as mentioned before.

	mkdir build
	cd build
	cmake -G "Unix Makefiles" ..
	make 

#### On Windows

Use Cmake's GUI to create a MSVC solution, and build it.

## Usage

### Execute

    USAGE ./build/SfMToyUI [options] <input-directory>
      -h [ --help ]                   produce help message
      -d [ --console-debug ] arg (=2) Debug output to console log level (0 = Trace,
                                      4 = Error).
      -v [ --visual-debug ] arg (=3)  Visual debug output to screen log level (0 = 
                                      All, 4 = None).
      -s [ --downscale ] arg (=1)     Downscale factor for input images
      -p [ --input-directory ] arg    Directory to find input images

### Datasets

Here's a place with some standard datasets for SfM: http://cvlabwww.epfl.ch/data/multiview/denseMVS.html

Also, you can use the "Crazy Horse" (A national memorial site in South Dakota) dataset, that I pictured myself, included in the repo.

### Other

Some relevant blog posts from over the years:
- http://www.morethantechnical.com/2016/10/17/structure-from-motion-toy-lib-upgrades-to-opencv-3/
- http://www.morethantechnical.com/2015/03/16/bootstrapping-planar-ar-tracking-without-markers-wcode/
- http://www.morethantechnical.com/2013/11/04/moving-to-qt-on-the-sfm-toy-library-project/
- http://www.morethantechnical.com/2012/08/09/checking-for-co-planarity-of-3d-points-in-opencv-wcode/
- http://www.morethantechnical.com/2012/08/09/decomposing-the-essential-matrix-using-horn-and-eigen-wcode/
- http://www.morethantechnical.com/2012/02/07/structure-from-motion-and-3d-reconstruction-on-the-easy-in-opencv-2-3-w-code/
- http://www.morethantechnical.com/2012/01/04/simple-triangulation-with-opencv-from-harley-zisserman-w-code/

## References

1. Multiple View Geometry in Computer Vision, Hartley, R. I. and Zisserman, A., 2004, Cambridge University Press [http://www.robots.ox.ac.uk/~vgg/hzbook/]
2. Modeling the World from Internet Photo Collections, N. Snavely, S. M. Seitz, R. Szeliski, IJCV 2007 [http://phototour.cs.washington.edu/ModelingTheWorld_ijcv07.pdf]
3. Triangulation, R.I. Hartley, P. Strum, 1997, Computer vision and image understanding [http://perception.inrialpes.fr/Publications/1997/HS97/HartleySturm-cviu97.pdf]
4. Recovering baseline and orientation from essential matrix, B.K.P. Horn, 1990, J. Optical Society of America [http://people.csail.mit.edu/bkph/articles/Essential_Old.pdf]
5. On benchmarking camera calibration and multi-view stereo for high resolution imagery. Strecha, Christoph, et al. IEEE Computer Vision and Pattern Recognition (CVPR) 2008. [http://infoscience.epfl.ch/record/126393/files/strecha_cvpr_2008.pdf]