This package implements real-time temporally consistent directional segmentation from Kinect RGB-D streams. It relies on the dpMMlowVar library for the actual implementation of the DDP-vMF-means algorithm. The approach and the algorithm is described in more detail on our project page and CVPR 2015 paper. The algorithms are described in more detail in the supplement. See below for install instructions.
If you use DP-vMF-means or DDP-vMF-means please cite:
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III.
"Small-Variance Nonparametric Clustering on the Hypersphere", In CVPR,
2015.
This code is dependent on Eigen3, Boost, CUDA, OpenCV and PCL. It has been tested on Ubuntu 14.04 with
- Eigen3 (3.0.5)
- Boost (1.54)
- CUDA (6.5)
- OpenCV (2.4)
- PCL (1.7)
This package uses the pods build system. Used widely at CSAIL MIT the build system makes it easy to break up software projects into small packages that can be checked out and compiled automatically (see below).
-
Linux:
Install Eigen3, Boost, OpenCV, and PCL
sudo apt-get install libeigen3-dev libboost-dev libopencv-dev libpcl-1.7-all-dev
Install the appropriate CUDA version matching with your nvidia drivers. On our machines we use
nvidia-340-dev
withlibcuda1-340 cuda-6-5 cuda-toolkit-6-5
Clone this repository and compile the code:
git clone [email protected]:jstraub/rtDDPvMF; cd rtDDPvMF; make checkout; make configure; make -j6; make install;
Note that this will checkout several other necessary repositories. To update all repositories run
make update; make configure; make -j6; make install;
Plug in your Kinect and run the following from the rtDDPvMF folder:
./build/bin/realtimeDDPvMF_openni --lambdaDeg 100
./build/bin/realtimeDDPvMF_openni -h
Allowed options:
-h [ --help ] produce help message
-K [ --K ] arg K for spkm clustering
-l [ --lambdaDeg ] arg lambda in degree for DP-vMF-means and
DDP-vMF-means
-b [ --beta ] arg beta parameter of the DDP-vMF-means
-s [ --nFramesSurvive ] arg number of frames a cluster survives without
observation
-o [ --out ] arg path to output file
-d [ --display ] display results
-B [ --B ] arg filter windows size B for guided filter
--eps arg epsilon parameter for guided filter
-f [ --f_d ] arg focal length of depth camera