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.
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 <string>] [--imagescale <float>]
[--threshold <float>] [--scale <float>]
[--max-camera-distance <float>]
[--consistency-checks <int>] [-k <int>] [-e <int>]
[-s <int>] [--incremental-meshing] [-c] [-b] [-v]
[--thread-image] [--thread-fusion]
[--thread-meshing] [-l <string>] [--] [--version]
[-h] <string> ...
Where:
--intrinsics <string>
File with Camera Matrix
--imagescale <float>
Image Depth Scale
--threshold <float>
Threshold
--scale <float>
Size of the Voxel
--max-camera-distance <float>
Maximum Camera Distance to Surface
--consistency-checks <int>
Number of Depth Consistency Checks
-k <int>, --imagestep <int>
Use every kth step
-e <int>, --endimage <int>
Number of the End Image
-s <int>, --startimage <int>
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 <string>, --loadmesh <string>
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.
<string> (accepted multiple times)
The File Names
