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https://github.com/tum-vision/fastfusion

Volumetric 3D Mapping in Real-Time on a CPU
https://github.com/tum-vision/fastfusion

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Volumetric 3D Mapping in Real-Time on a CPU

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README 

        
fastfusion

==========



Volumetric 3D Mapping in Real-Time on a CPU 



This code implements the approach for real-time 3D mapping on a CPU as

described in the following research paper:



http://vision.in.tum.de/_media/spezial/bib/steinbruecker_etal_icra2014.pdf



Volumetric 3D Mapping in Real-Time on a CPU (F. Steinbruecker, J. Sturm, D. Cremers), 

In Int. Conf. on Robotics and Automation, 2014.



![alt tag](http://vision.in.tum.de/_media/data/software/fastfusion_small.png)



Demo video:

http://youtu.be/7s9JePSln-M



```

@string{icra="Int. Conf. on Robotics and Automation"}

@inproceedings{Steinbruecker-etal-icra14,

  author = {F. Steinbruecker and J. Sturm and D. Cremers},

  title = {Volumetric 3D Mapping in Real-Time on a CPU},

  booktitle = icra,

  year = {2014},

  address = {Hongkong, China},

  titleurl = {steinbruecker_etal_icra2014.pdf},

  topic = {3D Reconstruction},

  keywords =  {RGB-D,Fusion,3d-reconstruction}

}

```



Installation

============



    $ git clone https://github.com/tum-vision/fastfusion.git`



    $ cd fastfusion

  

    $ cmake .



    $ make



Preparation of the data

======================



The software takes a text file as input which contains per file

- the camera pose

- the depth image filename

- the color image filename



You can either generate such a file yourself (e.g., by running

Christan Kerl's DVO SLAM:



http://vision.in.tum.de/data/software/dvo



available as open source on our homepage) or you can download 

sequences from the TUM RGB-D benchmark:



http://vision.in.tum.de/data/datasets/rgbd-dataset/



For simplicity, we take a pre-recorded sequence from the TUM

RGB-D benchmark.



    $ mkdir ~/data



    $ cd ~/data



    $ wget http://vision.in.tum.de/rgbd/dataset/freiburg3/rgbd_dataset_freiburg3_long_office_household.tgz



    $ tar xvzf rgbd_dataset_freiburg3_long_office_household.tgz



Now we need to generate the text file. For this, we use the associate.py tool from

the RGB-D benchmark website. We need to run it twice, as we join the

camera poses, the depth image list and the color image list into a single file:



    $ cd ~/fastfusion/



    $ ./associate.py ~/data/rgbd_dataset_freiburg3_long_office_household/groundtruth.txt ~/data/rgbd_dataset_freiburg3_long_office_household/depth.txt > tmp.txt



    $ ./associate.py tmp.txt ~/data/rgbd_dataset_freiburg3_long_office_household/rgb.txt > ~/data/rgbd_dataset_freiburg3_long_office_household/associate.txt



The resulting text file should look as follows:



    $ head ~/data/rgbd_dataset_freiburg3_long_office_household/associate.txt



```

1341847980.790000 -0.6832 2.6909 1.7373 0.0003 0.8617 -0.5072 -0.0145 1341847980.786879 depth/1341847980.786879.png 1341847980.786856 rgb/1341847980.786856.png

1341847980.820100 -0.6821 2.6914 1.7371 0.0003 0.8609 -0.5085 -0.0151 1341847980.822989 depth/1341847980.822989.png 1341847980.822978 rgb/1341847980.822978.png

1341847980.850000 -0.6811 2.6918 1.7371 0.0001 0.8610 -0.5084 -0.0159 1341847980.854690 depth/1341847980.854690.png 1341847980.854676 rgb/1341847980.854676.png

[..]

```



Running the code

================



    $ ./bin/onlinefusion ~/data/rgbd_dataset_freiburg3_long_office_household/associate.txt --thread-fusion



After some debugging output on the console, a window with a 3D viewer should open. To start the 

reconstruction process, press "S". 



If you run the program for the first time, press and hold the CTRL key and turn your scroll wheel. 

This is only needed once to "free" the camera viewpoint. After this, you can pan (right click) and 

rotate (left click) the view as you wish using your mouse.



Further options

===============



```

   ./bin/onlinefusion  [--intrinsics ] [--imagescale ]

                       [--threshold ] [--scale ]

                       [--max-camera-distance ]

                       [--consistency-checks ] [-k ] [-e ]

                       [-s ] [--incremental-meshing] [-c] [-b] [-v]

                       [--thread-image] [--thread-fusion]

                       [--thread-meshing] [-l ] [--] [--version]

                       [-h]  ...



Where: 



   --intrinsics 

     File with Camera Matrix



   --imagescale 

     Image Depth Scale



   --threshold 

     Threshold



   --scale 

     Size of the Voxel



   --max-camera-distance 

     Maximum Camera Distance to Surface



   --consistency-checks 

     Number of Depth Consistency Checks



   -k ,  --imagestep 

     Use every kth step



   -e ,  --endimage 

     Number of the End Image



   -s ,  --startimage 

     Number of the Start Image



   --incremental-meshing

     Perform incremental Meshing



   -c,  --loopclosures

     Read Multiple Trajectories and perform Loop Closures



   -b,  --buffer

     Buffer all Images



   -v,  --viewer

     Show a Viewer after Fusion



   --thread-image

     Thread reading the Images from Hard Disk



   --thread-fusion

     Thread the Fusion inside the Viewer



   --thread-meshing

     Thread the Meshing inside the Fusion



   -l ,  --loadmesh 

     Loads this mesh



   --,  --ignore_rest

     Ignores the rest of the labeled arguments following this flag.



   --version

     Displays version information and exits.



   -h,  --help

     Displays usage information and exits.



     (accepted multiple times)

     The File Names

```

![alt tag](http://vision.in.tum.de/_media/data/software/screenshot_fastfusion.png)